Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

Contents

 

lists

 

available

 

at

 

ScienceDirect

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

j

 

o u r n a

 

l

 

h

 

o

 

m e

 

p a g e :

 

w w w . e l s e v i e r . c o m / l o c a t e / s e m c d b

Review

Planarian

 

regeneration

 

as

 

a

 

model

 

of

 

anatomical

 

homeostasis:

 

Recent

progress

 

in

 

biophysical

 

and

 

computational

 

approaches

Michael

 

Levin a,b,∗,

 

Alexis

 

M.

 

Pietak a,

 

Johanna

 

Bischof a,b

a Allen

 

Discovery

 

Center

 

at

 

Tufts

 

University,

 

Medford,

 

MA

 

02155,

 

United

 

States

b Biology

 

Department,

 

Tufts

 

University,

 

Medford,

 

MA

 

02155,

 

United

 

States

a

 

r

 

t

 

i

 

c

 

l

 

e

 

i

 

n

 

f

 

o

Article

 

history:

Received

 

5

 

January

 

2018

Received

 

in

 

revised

 

form

 

3

 

April

 

2018

Accepted

 

6

 

April

 

2018

Available

 

online

 

1

 

May

 

2018

Keywords:
Planaria
Dugesia

 

japonica

Regeneration
Patterning
Morphostasis

a

 

b

 

s

 

t

 

r

 

a

 

c

 

t

Planarian

 

behavior,

 

physiology,

 

and

 

pattern

 

control

 

offer

 

profound

 

lessons

 

for

 

regenerative

 

medicine,

evolutionary

 

biology,

 

morphogenetic

 

engineering,

 

robotics,

 

and

 

unconventional

 

computation.

 

Despite

recent

 

advances

 

in

 

the

 

molecular

 

genetics

 

of

 

stem

 

cell

 

differentiation,

 

this

 

model

 

organism’s

 

remark-

able

 

anatomical

 

homeostasis

 

provokes

 

us

 

with

 

truly

 

fundamental

 

puzzles

 

about

 

the

 

origin

 

of

 

large-scale

shape

 

and

 

its

 

relationship

 

to

 

the

 

genome.

 

In

 

this

 

review

 

article,

 

we

 

first

 

highlight

 

several

 

deep

 

mysteries

about

 

planarian

 

regeneration

 

in

 

the

 

context

 

of

 

the

 

current

 

paradigm

 

in

 

this

 

field.

 

We

 

then

 

review

 

recent

progress

 

in

 

understanding

 

of

 

the

 

physiological

 

control

 

of

 

an

 

endogenous,

 

bioelectric

 

pattern

 

memory

that

 

guides

 

regeneration,

 

and

 

how

 

modulating

 

this

 

memory

 

can

 

permanently

 

alter

 

the

 

flatworm’s

 

target

morphology.

 

Finally,

 

we

 

focus

 

on

 

computational

 

approaches

 

that

 

complement

 

reductive

 

pathway

 

analy-

sis

 

with

 

synthetic,

 

systems-level

 

understanding

 

of

 

morphological

 

decision-making.

 

We

 

analyze

 

existing

models

 

of

 

planarian

 

pattern

 

control

 

and

 

highlight

 

recent

 

successes

 

and

 

remaining

 

knowledge

 

gaps

 

in

 

this

interdisciplinary

 

frontier

 

field.

©

 

2018

 

Elsevier

 

Ltd.

 

All

 

rights

 

reserved.

Contents

1.

 

Introduction

 

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126

1.1.

 

A

 

primer

 

on

 

planarians’

 

functional

 

features

 

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126

1.2.

 

Fundamental

 

knowledge

 

gaps .

 

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126

1.3.

 

Perspective

 

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128

2.

 

Physiological

 

controls

 

of

 

patterning .

 

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. 128

2.1.

 

Prediction

 

1:

 

ion

 

channels

 

and

 

voltage

 

gradients

 

are

 

involved

 

in

 

planarian

 

patterning

 

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128

2.2.

 

Prediction

 

2:

 

neurotransmitters

 

are

 

involved

 

in

 

planarian

 

patterning

 

control

 

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130

2.3.

 

Prediction

 

3:

 

anatomical

 

outcome

 

and

 

genetic

 

default

 

can

 

diverge

 

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130

2.4.

 

Prediction

 

4:

 

pattern

 

memory

 

can

 

be

 

over-written

 

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133

2.5.

 

Summary:

 

physiological

 

controls

 

of

 

growth

 

and

 

form

 

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134

3.

 

Computational

 

approaches

 

to

 

an

 

integrative

 

understanding

 

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134

3.1.

 

Current

 

state

 

of

 

the

 

art

 

in

 

understanding

 

regenerative

 

dynamics:

 

gradients

 

and

 

beyond

 

.

 

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135

3.2.

 

Advances

 

in

 

modeling

 

and

 

simulation:

 

testing

 

available

 

models

 

.

 

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135

3.3.

 

Tools

 

for

 

model

 

discovery

 

.

 

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137

4.

 

Conclusion

 

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138

Acknowledgements

 

.

 

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References

 

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138

∗ Corresponding

 

author

 

at:

 

Allen

 

Discovery

 

Center

 

at

 

Tufts

 

University,

 

200

 

College

Avenue,

 

Medford,

 

MA

 

02155,

 

United

 

States.

E-mail

 

address:

 

michael.levin@tufts.edu

 

(M.

 

Levin).

https://doi.org/10.1016/j.semcdb.2018.04.003
1084-9521/©

 

2018

 

Elsevier

 

Ltd.

 

All

 

rights

 

reserved.

914327a74b2eb3aa0c9c512d8ed73e06-html.html

126

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

1.

 

Introduction

1.1.

 

A

 

primer

 

on

 

planarians’

 

functional

 

features

Planarian

 

flatworms

 

are

 

free-living

 

bilaterian

 

organisms

 

with

 

a

complex

 

set

 

of

 

organ

 

systems

 

and

 

cell

 

types

 

[1,2].

 

Planaria

 

possess

rich

 

behavioral

 

repertoires

 

that

 

include

 

sensing

 

of

 

a

 

wide

 

range

 

of

environmental

 

cues

 

ranging

 

from

 

chemicals

 

[3]

 

to

 

gravity

 

[4],

 

to

weak

 

gamma

 

radiation

 

[5].

 

Though

 

planaria

 

have

 

a

 

true

 

brain

 

[6,7],

the

 

real

 

wonder

 

of

 

this

 

remarkable

 

model

 

system

 

is

 

revealed

 

most

clearly

 

when

 

one

 

considers

 

the

 

robust

 

decision-making

 

capabili-

ties

 

of

 

its

 

individual

 

somatic

 

pieces

 

[8].

 

Amputated

 

fragments

 

of

planaria

 

regenerate

 

into

 

a

 

complete

 

worm,

 

growing

 

precisely

 

what

is

 

missing

 

 

no

 

more

 

and

 

no

 

less

 

 

with

 

events

 

at

 

the

 

wound

 

edge

coordinated

 

tightly

 

with

 

body-wide

 

remodeling

 

of

 

the

 

intact

 

tis-

sue

 

to

 

ensure

 

that

 

a

 

perfectly

 

proportioned

 

animal

 

results

 

within

 

a

week

 

after

 

it

 

is

 

cut

 

along

 

any

 

plane

 

[9].

 

Every

 

piece

 

of

 

a

 

planarian

 

is

able

 

to

 

grow

 

and

 

remodel

 

towards

 

its

 

species-specific

 

large-scale

pattern,

 

stopping

 

precisely

 

when

 

that

 

specific

 

anatomical

 

pattern

(the

 

target

 

morphology)

 

is

 

achieved.

 

This

 

is

 

most

 

obvious

 

in

 

the

head-tail

 

polarity

 

of

 

their

 

primary

 

axis:

 

like

 

a

 

bar

 

magnet

 

cut

 

into

pieces

 

(Fig.

 

1

 

A),

 

each

 

piece

 

determines

 

head/tail

 

identity

 

at

 

the

wound

 

based

 

on

 

an

 

invariant

 

axial

 

polarity,

 

regenerating

 

into

 

a

worm

 

with

 

one

 

head

 

and

 

one

 

tail.

Planarians’

 

unparalleled

 

pattern

 

homeostasis

 

 

the

 

ability

 

to

adjust

 

cellular

 

activity

 

to

 

a

 

large-scale

 

anatomical

 

specification

[10,11]

 

 

is

 

an

 

on-going

 

process,

 

not

 

an

 

injury-specific

 

one.

 

Starving

animals

 

will

 

shrink,

 

and

 

well-fed

 

animals

 

will

 

grow,

 

repeating

 

this

process

 

ad

 

infinitum

 

while

 

maintaining

 

correct

 

body

 

proportions

[12].

 

Because

 

of

 

this

 

capability,

 

planaria

 

have

 

conquered

 

aging.

While

 

single

 

cells

 

senesce

 

and

 

die

 

within

 

relatively

 

short

 

time

spans

 

(

∼1

 

month)

 

[13–18],

 

the

 

animal

 

regenerates

 

tissues

 

con-

tinuously;

 

planaria

 

like

 

D.

 

japonica

 

appear

 

to

 

be

 

immortal

 

at

 

the

level

 

of

 

the

 

individual.

 

Planaria

 

are

 

the

 

champions

 

of

 

life-long

 

func-

tional

 

health

 

and

 

damage

 

repair.

 

However,

 

unlike

 

simpler

 

forms

of

 

life

 

such

 

as

 

bacteria

 

or

 

Hydra,

 

planaria

 

combine

 

these

 

abilities

with

 

significant

 

cognitive

 

potential

 

 

they

 

can

 

learn

 

in

 

instrumen-

tal

 

and

 

classical

 

conditioning

 

contexts

 

[19–24].

 

In

 

keeping

 

with

their

 

incredible

 

somatic

 

plasticity,

 

they

 

can

 

transfer

 

information

between

 

the

 

body

 

and

 

the

 

brain

 

[25–28],

 

and

 

reprogram

 

cancerous

tissue

 

when

 

regeneration

 

is

 

activated

 

[29,30].

Planaria

 

serve

 

as

 

a

 

proof-of-principle

 

 

showing

 

what

 

is

 

possible

for

 

biological

 

systems

 

to

 

achieve.

 

Therefore,

 

truly

 

understanding

planaria

 

would

 

crack

 

open

 

most

 

of

 

the

 

pressing

 

problems

 

in

 

the

fields

 

of

 

aging,

 

regenerative

 

medicine,

 

cancer,

 

and

 

primitive

 

cog-

nition.

 

Beyond

 

biology,

 

they

 

also

 

serve

 

as

 

a

 

design

 

challenge

 

for

fields

 

such

 

as

 

morphogenetic

 

engineering

 

[31,32],

 

unconventional

computation

 

[33–35],

 

and

 

soft

 

robotics

 

[36,37],

 

which

 

look

 

to

 

the

biological

 

world

 

for

 

clues

 

as

 

to

 

designing

 

robust,

 

parallel,

 

fault-

tolerant

 

systems.

 

The

 

planarian’s

 

ability

 

to

 

integrate

 

patterning

 

and

functional

 

control

 

is

 

a

 

superb

 

example

 

of

 

morphological

 

compu-

tation

 

[38]

 

and

 

distributed

 

decision-making

 

[39,40].

 

The

 

dynamic

adaptability

 

of

 

planaria

 

dwarfs

 

engineers’

 

efforts

 

on

 

this

 

front

 

 

no

human-designed

 

systems

 

even

 

come

 

close

 

to

 

their

 

capabilities

 

yet.

1.2.

 

Fundamental

 

knowledge

 

gaps

“No

 

paradox,

 

no

 

progress.”

 

-

 

Niels

 

Bohr

As

 

befits

 

a

 

model

 

system

 

with

 

fundamental

 

lessons

 

to

 

teach,

planaria

 

starkly

 

reveal

 

the

 

large

 

gaps

 

in

 

current

 

knowledge.

 

While

much

 

excellent

 

work

 

has

 

drilled

 

deeply

 

into

 

the

 

molecular

 

genetics

of

 

stem

 

cell

 

regulation

 

[41,42]

 

and

 

specific

 

pathways

 

necessary

 

for

regeneration

 

[43,44],

 

we

 

are

 

still

 

largely

 

missing

 

an

 

understand-

ing

 

of

 

the

 

dynamics

 

that

 

are

 

sufficient

 

to

 

build

 

an

 

organism

 

with

the

 

observed

 

remodeling

 

and

 

repair

 

functions.

 

Here,

 

we

 

argue

 

that

some

 

of

 

the

 

most

 

fascinating

 

aspects

 

of

 

this

 

field

 

concern

 

not

 

only

the

 

mechanisms

 

involved

 

at

 

the

 

cellular

 

and

 

molecular

 

levels,

 

but

also

 

the

 

algorithms

 

that

 

harness

 

individual

 

cell

 

behaviors

 

toward

specific

 

large-scale

 

anatomical

 

outcomes.

 

We

 

begin

 

by

 

highlight-

ing

 

a

 

few

 

of

 

the

 

mysteries

 

that

 

still

 

confront

 

us

 

despite

 

the

 

recent

advances

 

in

 

planarian

 

molecular

 

biology.

Establishing

 

correct

 

anterior-posterior

 

(AP)

 

polarity

 

involves

making

 

sure

 

every

 

fragment

 

cut

 

from

 

a

 

planarian

 

ends

 

up

 

with

exactly

 

one

 

head

 

and

 

one

 

tail,

 

at

 

the

 

appropriate

 

locations

 

(set-

ting

 

aside

 

for

 

now

 

the

 

issue

 

of

 

orienting

 

and

 

scaling

 

them

 

correctly,

giving

 

them

 

the

 

right

 

shape,

 

and

 

stopping

 

when

 

they

 

are

 

done).

Planaria

 

axially

 

pattern

 

each

 

fragment

 

no

 

matter

 

where

 

it

 

came

from

 

in

 

the

 

parent

 

structure

 

(Fig.

 

1A’)

 

 

the

 

challenging

 

nature

 

of

this

 

process

 

becomes

 

clear

 

when

 

one

 

considers

 

a

 

bisected

 

worm

(Fig.

 

1A”).

 

One

 

wound

 

site

 

will

 

make

 

a

 

head

 

while

 

the

 

other

 

will

make

 

a

 

tail;

 

the

 

cells

 

in

 

those

 

two

 

wound

 

sites

 

were

 

adjacent

 

neigh-

bors

 

(before

 

an

 

arbitrary

 

cut

 

separated

 

them)

 

and

 

yet

 

go

 

on

 

to

 

make

radically

 

different

 

anatomical

 

structures.

 

A

 

fundamental

 

challenge

is

 

to

 

understand

 

the

 

decision-making

 

process

 

that

 

occurs

 

at

 

each

wound

 

site

 

 

what

 

are

 

the

 

cells

 

measuring,

 

and

 

from

 

how

 

far

 

away,

to

 

determine

 

anatomical

 

identity

 

of

 

the

 

new

 

tissue

 

to

 

be

 

made?

This

 

is

 

difficult

 

for

 

simple

 

gradient

 

models

 

to

 

explain

 

(as

 

the

 

wound

cells

 

had

 

identical

 

positional

 

information

 

before

 

the

 

cut,

 

Fig.

 

1A”,

green

 

circles);

 

‘head-inhibits-head’

 

models

 

of

 

this

 

phenomenon

 

run

afoul

 

of

 

the

 

fact

 

that

 

a

 

brain

 

has

 

no

 

trouble

 

building

 

another

 

brain

right

 

next

 

to

 

it,

 

if

 

the

 

head

 

is

 

amputated

 

longitudinally,

 

as

 

well

as

 

being

 

unable

 

to

 

explain

 

axial

 

patterning

 

in

 

mid-fragments

 

with

open

 

wounds

 

on

 

both

 

sides.

The

 

second

 

puzzle

 

concerns

 

the

 

conspicuous

 

lack

 

of

 

stable

lines

 

of

 

planarian

 

patterning

 

mutants.

 

Most

 

other

 

model

 

organ-

isms

 

 

Drosophila,

 

C.

 

elegans,

 

chick,

 

mouse,

 

zebrafish,

 

and

 

humans

 

all

 

offer

 

stably

 

transmitted

 

lines

 

with

 

characteristic

 

and

 

strik-

ing

 

anatomical

 

deviations

 

from

 

wild-type

 

(patterning

 

mutants)

that

 

can

 

be

 

studied

 

to

 

forge

 

a

 

link

 

between

 

genetics

 

and

 

anatomy

(central

 

to

 

understanding

 

the

 

genotype-phenotype

 

relationship

 

in

evolution).

 

It

 

is

 

rarely

 

mentioned

 

in

 

our

 

field

 

that,

 

despite

 

almost

120

 

years

 

of

 

every

 

conceivable

 

experiment

 

including

 

irradiation,

with

 

the

 

exception

 

of

 

a

 

physiologically-induced

 

double-headed

 

line

described

 

below,

 

there

 

are

 

no

 

planarian

 

mutant

 

lines

 

with

 

patterns

(in

 

shape,

 

number,

 

or

 

placement

 

of

 

specific

 

organs)

 

that

 

differ

 

from

the

 

standard

 

species-specific

 

target

 

morphology.

 

Why?

The

 

answer

 

might

 

be

 

linked

 

to

 

the

 

third

 

puzzle:

 

their

 

remark-

able

 

anatomical

 

stability

 

in

 

the

 

face

 

of

 

a

 

highly

 

variable

 

genome.

Weissmann’s

 

Barrier

 

(Fig.

 

1B,

 

B’)

 

does

 

not

 

apply

 

to

 

species

 

like

 

D.

japonica

 

which

 

reproduce

 

primarily

 

asexually

 

by

 

fission

 

and

 

regen-

eration.

 

In

 

this

 

case,

 

any

 

mutation

 

that

 

does

 

not

 

kill

 

a

 

neoblast

 

is

propagated

 

to

 

the

 

next

 

generation

 

and

 

can

 

expand

 

into

 

a

 

clonal

line

 

(Fig.

 

1B”,

 

B”’).

 

This

 

will

 

be

 

especially

 

true

 

of

 

the

 

inevitable

dominant

 

mutations,

 

which

 

increase

 

relative

 

cellular

 

fitness

 

and

could

 

become

 

something

 

akin

 

to

 

cancer

 

stem

 

cells.

 

This

 

somatic

inheritance

 

(Fig.

 

1C–C”’)

 

is

 

predicted

 

to

 

generate

 

extremely

 

diver-

gent

 

genomes

 

over

 

time

 

[45];

 

indeed

 

not

 

only

 

are

 

some

 

planarian

species

 

mixoploid

 

(not

 

every

 

cell

 

has

 

the

 

same

 

chromosome

 

num-

ber)

 

[46],

 

but

 

they

 

also

 

accumulate

 

immense

 

amount

 

of

 

change:

 

up

to

 

74%

 

in

 

protein-coding

 

genes

 

[47].

 

Indeed,

 

large

 

numbers

 

of

 

muta-

tions

 

have

 

been

 

found

 

both

 

outside

 

and

 

within

 

gene-coding

 

regions,

including

 

many

 

amino

 

acid

 

substitutions

 

and

 

non-synonymous

SNPs.

 

Furthermore,

 

a

 

recent

 

analysis

 

of

 

the

 

genome

 

of

 

the

 

pla-

naria

 

Schmidtea

 

mediterranea

 

revealed

 

that

 

many

 

essential

 

genes

appear

 

to

 

be

 

missing

 

from

 

the

 

genome,

 

including

 

components

 

of

many

 

core

 

pathways

 

ranging

 

from

 

cell

 

division,

 

to

 

DNA

 

repair

and

 

metabolism

 

[48].

 

And

 

yet,

 

these

 

animals

 

regenerate

 

under

control

 

conditions

 

with

 

100%

 

anatomical

 

fidelity,

 

making

 

perfect

planaria

 

each

 

time

 

despite

 

their

 

messy

 

genomes.

 

How

 

is

 

it

 

possible

for

 

the

 

genome

 

to

 

accumulate

 

somatic

 

mutations

 

over

 

hundreds

of

 

millions

 

of

 

years

 

and

 

yet

 

maintain

 

perfect

 

anatomical

 

fidelity?

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

127

Fig.

 

1.

 

Planarian

 

regeneration:

 

fundamental

 

puzzles

 

of

 

pattern

 

control.

(A)

 

Bar

 

magnets

 

illustrate

 

a

 

basic

 

property

 

of

 

polarity

 

re-scaling:

 

having

 

a

 

North

 

and

 

South

 

pole,

 

a

 

magnet

 

can

 

be

 

cut

 

into

 

pieces

 

and

 

each

 

piece

 

reorganizes

 

its

 

polarity

 

to

likewise

 

have

 

a

 

North

 

and

 

South

 

pole

 

by

 

orientating

 

small

 

magnetic

 

domains

 

into

 

large-scale

 

axial

 

patterning.

(A’)

 

Planarian’s

 

anterior-posterior

 

(AP)

 

axis

 

likewise

 

re-scales:

 

every

 

piece

 

cut

 

from

 

a

 

planarian

 

(D.

 

japonica

 

shown

 

here)

 

regenerates

 

a

 

head

 

and

 

tail

 

at

 

the

 

correct

 

end.

(A”)

 

Common

 

models

 

of

 

axial

 

patterning

 

postulate

 

a

 

chemical

 

gradient

 

that

 

indicates

 

positional

 

information

 

for

 

cells

 

along

 

the

 

AP

 

axis.

 

The

 

ability

 

of

 

cells

 

on

 

either

 

side

 

of

 

a

bisection

 

to

 

develop

 

distinct

 

anatomical

 

fates

 

(head

 

vs.

 

tail),

 

even

 

though

 

they

 

began

 

as

 

adjacent

 

neighbors

 

with

 

identical

 

positional

 

information

 

(green

 

circles),

 

suggests

the

 

need

 

for

 

long-range

 

communication

 

across

 

the

 

fragment

 

so

 

that

 

decisions

 

at

 

the

 

wound

 

could

 

be

 

made

 

based

 

on

 

the

 

rest

 

of

 

the

 

fragment.

914327a74b2eb3aa0c9c512d8ed73e06-html.html

128

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

This

 

paradox

 

is

 

pointing

 

to

 

a

 

fundamental

 

lack

 

of

 

knowledge

 

of

“where

 

pattern

 

comes

 

from”

 

and

 

what

 

part

 

genetics

 

plays

 

in

 

the

process.

 

We

 

are

 

currently

 

investigating

 

this

 

issue

 

by

 

raising

 

gener-

ations

 

of

 

planaria

 

in

 

high

 

doses

 

of

 

various

 

mutagens,

 

to

 

fully

 

probe

the

 

stability

 

of

 

the

 

animals’

 

pattern

 

homeostasis

 

under

 

genetic

change.

The

 

fourth

 

puzzle

 

to

 

consider

 

is

 

a

 

thought

 

experiment.

 

Suppose

 

a

hybrid

 

was

 

made

 

between

 

planaria

 

with

 

two

 

distinct

 

head

 

shapes:

removing

 

half

 

of

 

the

 

neoblasts

 

of

 

one

 

species

 

of

 

planaria,

 

and

 

pop-

ulating

 

it

 

with

 

50%

 

of

 

the

 

neoblasts

 

from

 

another

 

species

 

(Fig.

 

1D).

After

 

the

 

transplanted

 

neoblasts

 

adjust

 

to

 

their

 

new

 

home,

 

the

 

head

is

 

then

 

amputated

 

(Fig.

 

1D’,

 

D”).

 

The

 

neoblasts

 

will

 

start

 

building

a

 

new

 

head

 

 

but

 

what

 

shape

 

will

 

form

 

(Fig.

 

1D”’)?

 

Is

 

one

 

set

 

of

neoblasts

 

dominant

 

over

 

the

 

other,

 

will

 

the

 

resulting

 

head

 

be

 

an

intermediate

 

shape,

 

or

 

will

 

the

 

remodeling

 

never

 

cease,

 

as

 

each

set

 

of

 

neoblasts

 

is

 

never

 

given

 

the

 

“stop”

 

signal

 

derived

 

from

 

hav-

ing

 

a

 

complete,

 

normally-shaped

 

head

 

appropriate

 

to

 

its

 

species?

The

 

value

 

of

 

this

 

thought

 

experiment

 

is

 

not

 

so

 

much

 

only

 

in

 

the

answer,

 

but

 

in

 

the

 

fact

 

that

 

we

 

currently

 

have

 

no

 

models

 

with

 

which

we

 

could

 

make

 

a

 

prediction

 

about

 

this

 

basic

 

scenario.

 

The

 

com-

bined

 

communities’

 

best

 

quantitative

 

models,

 

including

 

extensive

RNA-seq

 

profiling,

 

do

 

not

 

constrain

 

the

 

outcome

 

of

 

such

 

experi-

ments

 

 

they

 

are

 

silent

 

about

 

shape

 

and

 

what

 

determines

 

it,

 

and

it

 

is

 

not

 

yet

 

clear

 

how

 

much

 

of

 

the

 

necessary

 

information

 

is

 

gen-

erated

 

by

 

the

 

soma

 

and

 

how

 

much

 

by

 

the

 

neoblasts.

 

Thus,

 

our

field

 

lacks

 

theoretical

 

frameworks

 

for

 

thinking

 

about

 

large-scale

states

 

(e.g.,

 

head

 

shape)

 

as

 

targets

 

and

 

stop

 

conditions

 

for

 

pattern

homeostasis

 

loops

 

that

 

regulate

 

cell

 

behavior.

 

This

 

is

 

a

 

necessity

 

if

we

 

are

 

to

 

understand

 

how

 

cells

 

know

 

what

 

to

 

build

 

and

 

when

 

to

stop.

1.3.

 

Perspective

We

 

highlight

 

these

 

puzzles

 

as

 

examples

 

of

 

questions

 

that

emphasize

 

major

 

knowledge

 

gaps,

 

and

 

thus

 

opportunities,

 

in

 

this

field.

 

We

 

argue

 

that

 

significant

 

new

 

conceptual

 

and

 

technological

approaches

 

must

 

be

 

added

 

to

 

the

 

highly

 

successful

 

mainstream

efforts

 

to

 

understand

 

specific

 

gene

 

products

 

and

 

cell-level

 

phe-

notypes.

 

Here,

 

we

 

use

 

planarian

 

regeneration

 

as

 

a

 

lens

 

through

which

 

we

 

can

 

view

 

several

 

broad

 

issues

 

of

 

biological

 

computation,

pattern

 

control,

 

and

 

the

 

genotype-phenotype

 

relation.

 

Advances

in

 

biochemical

 

and

 

genetic

 

controls

 

of

 

regeneration

 

have

 

been

expertly

 

reviewed

 

elsewhere

 

[44,49–51].

 

Focusing

 

on

 

synthesis

 

and

understanding

 

global

 

morphological

 

decision-making

 

in

 

this

 

model

system,

 

we

 

review

 

recent

 

developments

 

in

 

this

 

field

 

that

 

have

 

the

potential

 

to

 

drive

 

progress

 

on

 

fundamental

 

aspects

 

of

 

the

 

origin

and

 

control

 

of

 

growth

 

and

 

form.

 

These

 

include

 

(1)

 

endogenous

physiological

 

signals

 

that

 

underlie

 

pattern

 

control:

 

bioelectric

 

and

neurotransmitter-mediated

 

signaling

 

mechanisms

 

in

 

numerous

cell

 

types,

 

which

 

enable

 

long-range

 

coordination

 

and

 

morpholog-

ical

 

decision-making

 

by

 

cell

 

collectives

 

[40];

 

and

 

(2)

 

advances

 

in

computational

 

modeling

 

and

 

automated

 

model

 

discovery,

 

which

are

 

helping

 

to

 

understand

 

the

 

algorithms

 

by

 

which

 

planarian

 

shape

is

 

controlled.

2.

 

Physiological

 

controls

 

of

 

patterning

Regeneration

 

of

 

significant

 

injury

 

requires

 

rebuilding

 

structures

that

 

are

 

properly

 

coordinated

 

in

 

position,

 

orientation,

 

and

 

size

with

 

the

 

large-scale

 

anatomy

 

of

 

the

 

remaining

 

body,

 

which

 

implies

that

 

cells

 

need

 

non-local

 

information

 

to

 

make

 

patterning

 

deci-

sions

 

(Fig.

 

1A’).

 

The

 

ability

 

to

 

re-create

 

the

 

same

 

structure

 

time

and

 

again

 

can

 

be

 

understood

 

as

 

a

 

“pattern

 

memory”

 

[39,52],

 

while

the

 

ability

 

to

 

reach

 

the

 

same

 

correct

 

pattern

 

from

 

different

 

initial

starting

 

conditions

 

(location

 

and

 

extent

 

of

 

damage)

 

implies

 

a

 

robust

goal-directed

 

process.

 

One

 

way

 

to

 

think

 

about

 

the

 

remarkable

decision-making

 

properties

 

of

 

planarian

 

tissues

 

is

 

to

 

consider

 

the

algorithms

 

and

 

molecular

 

mechanisms

 

exploited

 

by

 

brains

 

 

our

best

 

example

 

of

 

biological

 

systems

 

that

 

implement

 

memory,

 

dis-

tributed

 

processing,

 

decision-making,

 

and

 

flexible

 

goal-achieving

cell

 

networks.

 

Interestingly,

 

planaria

 

were

 

one

 

of

 

the

 

earliest

model

 

systems

 

in

 

which

 

data

 

suggested

 

roles

 

for

 

ion-

 

and

 

voltage-

mediated

 

processes

 

in

 

guiding

 

regeneration

 

[53–57].

 

Since

 

then,

advances

 

in

 

developmental

 

bioelectricity

 

(reviewed

 

in

 

[58,59])

have

 

driven

 

hypotheses

 

about

 

the

 

role

 

of

 

ion

 

channels

 

in

 

guiding

cell

 

behavior,

 

and

 

more

 

broadly,

 

about

 

the

 

relationships

 

between

the

 

activity

 

of

 

multi-cellular

 

electric

 

circuits

 

and

 

developmental

morphospace

 

[60,61].

 

This

 

perspective

 

made

 

several

 

specific

 

and

counter-intuitive

 

predictions,

 

driving

 

new

 

experiments

 

that

 

com-

plement

 

the

 

biochemical/genetic

 

research

 

programs.

 

Recent

 

work

has

 

tested

 

some

 

of

 

these

 

predictions,

 

uncovering

 

novel

 

biology

 

in

the

 

planarian

 

model

 

system.

2.1.

 

Prediction

 

1:

 

ion

 

channels

 

and

 

voltage

 

gradients

 

are

 

involved

in

 

planarian

 

patterning

Neurons

 

compute

 

and

 

transmit

 

information

 

long-distance

 

by

virtue

 

of

 

electrical

 

signaling.

 

It

 

is

 

now

 

well-recognized

 

that

 

even

neural

 

computations

 

can

 

involve

 

graded

 

(not

 

spiking)

 

potentials

[62–66],

 

which

 

are

 

similar

 

to

 

the

 

slower

 

non-neural

 

bioelectric

events

 

that

 

operate

 

in

 

regeneration

 

and

 

development

 

(Fig.

 

2A).

Could

 

non-neural

 

somatic

 

tissues

 

be

 

performing

 

information-

processing

 

tasks

 

by

 

exploiting

 

ion

 

flows,

 

albeit

 

in

 

different

 

ways

and

 

on

 

different

 

timescales

 

than

 

brains?

 

While

 

developmental

 

biol-

ogists

 

are

 

not

 

yet

 

used

 

to

 

thinking

 

of

 

non-neural

 

tissues

 

making

decisions,

 

the

 

emerging

 

field

 

of

 

primitive

 

cognition

 

[67,68]

 

and

 

the

recent

 

data

 

on

 

the

 

phylogeny

 

of

 

ion

 

channel

 

and

 

neurotransmit-

ter

 

signals

 

[69–71]

 

have

 

highlighted

 

the

 

fact

 

that

 

brains

 

did

 

not

invent

 

their

 

tricks

 

de

 

novo

 

 

the

 

basic

 

machinery

 

of

 

bioelectrical

computation

 

(Fig.

 

2A’)

 

was

 

present

 

very

 

early

 

on

 

in

 

evolution

 

and

is

 

ubiquitous

 

in

 

animal,

 

plant,

 

and

 

fungal

 

bodies.

 

Even

 

before

 

mul-

ticellularity,

 

cells

 

were

 

using

 

ion

 

channels

 

and

 

neurotransmitters

to

 

process

 

information

 

and

 

communicate;

 

from

 

bacteria

 

and

 

fungi

to

 

the

 

earliest

 

metazoans,

 

cells

 

exploited

 

ion

 

currents

 

to

 

regulate

individual

 

and

 

group

 

behaviors

 

(data

 

in

 

vertebrate

 

as

 

well

 

as

 

inver-

tebrate

 

systems

 

are

 

reviewed

 

in

 

[59,72–74]).

Research

 

into

 

bioelectric

 

aspects

 

of

 

patterning

 

has

 

a

 

rich

 

his-

tory

 

[75,76].

 

The

 

ideal

 

fit

 

of

 

bioelectric

 

circuits

 

to

 

the

 

control

 

of

regeneration

 

did

 

not

 

escape

 

prescient

 

early

 

workers

 

such

 

as

 

T.

 

H.

Morgan,

 

who

 

postulated

 

electrical

 

polarity

 

to

 

underlie

 

regenerative

axial

 

polarity

 

[77,78],

 

and

 

C.

 

M.

 

Child,

 

who

 

focused

 

on

 

physiologi-

(B,

 

C)

 

For

 

most

 

metazoans,

 

sexual

 

reproduction

 

results

 

in

 

Weissmann’s

 

barrier

 

 

somatic

 

mutations

 

(B)

 

do

 

not

 

persist

 

into

 

the

 

next

 

generation

 

(B’).

 

However,

 

planaria

 

such

 

as

D.

 

japonica

 

largely

 

reproduce

 

through

 

fission;

 

thus,

 

mutations

 

in

 

neoblasts

 

anywhere

 

in

 

the

 

worm

 

body

 

(B”)

 

that

 

do

 

not

 

kill

 

the

 

neoblast

 

persist

 

into

 

the

 

next

 

generation

 

upon

fission

 

(B”’).

 

Within

 

that

 

individual

 

(C),

 

the

 

progeny

 

of

 

the

 

neoblast

 

inherit

 

the

 

mutation(s),

 

which

 

spread

 

throughout

 

the

 

body

 

(C’)

 

and

 

are

 

propagated

 

to

 

both

 

offspring

 

of

 

the

next

 

fission

 

event

 

(C”).

 

This

 

gives

 

rise

 

to

 

a

 

fundamental

 

puzzle

 

about

 

the

 

relationship

 

between

 

genetics

 

and

 

patterning:

 

over

 

hundreds

 

of

 

millions

 

of

 

years,

 

planarian

 

lineages

have

 

accumulated

 

diverse

 

mutations

 

in

 

their

 

bodies;

 

despite

 

the

 

resulting

 

very

 

messy

 

genomes,

 

planaria

 

regenerate

 

with

 

100%

 

anatomical

 

fidelity

 

and

 

offer

 

no

 

genetic

 

lines

of

 

patterning

 

mutants.

(D)

 

A

 

thought

 

experiment

 

involving

 

two

 

different

 

species

 

of

 

flatworms

 

with

 

distinct

 

head

 

shapes,

 

illustrates

 

knowledge

 

gaps

 

in

 

the

 

field.

 

If

 

one

 

of

 

the

 

worms

 

is

 

irradiated

so

 

that

 

half

 

of

 

its

 

neoblasts

 

are

 

killed

 

(D’),

 

and

 

neoblasts

 

from

 

another

 

worm

 

type

 

are

 

injected

 

into

 

this

 

host

 

(D”),

 

what

 

kind

 

of

 

head

 

shape

 

will

 

this

 

hybrid

 

worm

 

regenerate

after

 

amputation

 

(D”’)?

 

Existing

 

models

 

do

 

not

 

make

 

a

 

prediction

 

as

 

to

 

whether

 

a

 

dominant

 

shape,

 

a

 

combination

 

shape,

 

or

 

continuous

 

remodeling

 

will

 

result.

Panels

 

B–C”’

 

were

 

made

 

by

 

Jeremy

 

Guay

 

of

 

Peregrine

 

Creative.

 

Panels

 

D-D”’

 

are

 

used

 

with

 

permission

 

from

 

[8].

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

129

Fig.

 

2.

 

Bioelectric

 

signaling

 

among

 

somatic

 

tissues.

(A)

 

Neurons

 

implement

 

memory

 

and

 

distributed

 

decision-making

 

by

 

virtue

 

of

 

electrical

 

potentials

 

(Vmem)

 

set

 

by

 

ion

 

channels,

 

which

 

are

 

propagated

 

to

 

neighboring

 

cells

 

via

electrochemical

 

synapses

 

known

 

as

 

gap

 

junctions.

(A’)

 

The

 

same

 

machinery

 

is

 

present

 

in

 

most

 

cells,

 

where

 

ion

 

channels

 

and

 

pumps

 

set

 

Vmem,

 

and

 

gap

 

junctions

 

allow

 

its

 

propagation

 

to

 

some

 

neighboring

 

cells.

(B)

 

Tissues

 

sustain

 

physiological

 

compartments,

 

whose

 

borders

 

and

 

patterns

 

of

 

small

 

molecule

 

connectivity

 

that

 

are

 

driven

 

by

 

the

 

complex

 

gating

 

of

 

ion

 

channels

 

and

 

gap

junctions.

 

As

 

in

 

the

 

central

 

nervous

 

system,

 

neurotransmitters

 

are

 

among

 

the

 

key

 

small

 

molecule

 

morphogens

 

moved

 

across

 

tissues

 

by

 

bioelectric

 

properties.

(B’)

 

These

 

dynamics

 

result

 

in

 

spatio-temporal

 

distributions

 

of

 

resting

 

potential

 

across

 

anatomical

 

distances

 

(shown

 

here

 

in

 

a

 

planarian)

 

 

bioelectrical

 

prepatterns

 

that

underlie

 

subsequent

 

gene

 

expression

 

and

 

other

 

cell

 

behaviors

 

during

 

regeneration

 

and

 

development.

Panels

 

A,

 

A’,

 

and

 

B

 

were

 

created

 

by

 

Jeremy

 

Guay

 

of

 

Peregrine

 

Creative.

cal

 

gradients

 

in

 

pattern

 

control

 

[79].

 

Subsequent

 

work

 

used

 

applied

electric

 

fields

 

and

 

biochemical

 

analysis

 

to

 

manipulate

 

planarian

head/tail

 

polarity

 

[55,56,80,81]

 

and

 

suggested

 

the

 

electrophoretic

movement

 

of

 

morphogens

 

during

 

this

 

process

 

[82,83]

 

 

a

 

scheme

that

 

applies

 

also

 

to

 

vertebrate

 

left-right

 

axial

 

patterning

 

[84].

Recent

 

work

 

(summarized

 

in

 

Tables

 

1–3)

 

has

 

moved

 

beyond

applied

 

electric

 

fields

 

to

 

probe

 

the

 

regenerative

 

involvement

 

of

proteins

 

and

 

signaling

 

molecules

 

most

 

often

 

associated

 

with

 

the

nervous

 

system.

 

Bioelectric

 

signals

 

(both

 

slow

 

and

 

rapid)

 

occur

among

 

all

 

cell

 

types,

 

not

 

just

 

neurons,

 

and

 

are

 

driven

 

by

 

several

914327a74b2eb3aa0c9c512d8ed73e06-html.html

130

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

Table

 

1

Cell-level

 

properties/behaviors

 

controlled

 

by

 

bioelectric

 

events.

Cellular

 

properties

 

References

Proliferation

 

and

 

cell

 

cycle

 

progression

 

[194–207]

Apoptosis

 

[208–212]

Migration

 

and

 

orientation

 

[213–222]

Differentiation

 

[223–228]

De-differentiation

 

[202,205,207,229]

Table

 

2

Experimental

 

data

 

implicating

 

endogenous

 

bioelectric

 

signal

 

roles

 

in

morphogenesis.

Developmental

 

Role

 

Species/model
system

References

Cellular

 

polarization

 

(anatomical

asymmetry

 

of

 

cell

 

or

epithelium)

Alga

 

Fucus,

 

yeast

 

[230,231]

Migration

 

of

 

neurons

 

and

positional

 

information

Chick,

 

Amphibia

 

[232,233]

Patterning

 

in

 

gastrulation,

neurulation,

 

and

organogenesis

Chick,

 

axolotl,

 

frog

 

[90,232,234–237]

Directional

 

transport

 

of

 

maternal

components

 

into

 

the

 

oocyte

Moth,

 

Drosophila

 

[238]

Growth

 

control

 

and

 

size

determination

segmented

 

worms

 

[239]

Neural

 

differentiation

 

Xenopus

 

embryo

 

[225,240]

Polarity

 

during

 

regeneration

 

Planaria,

 

plants,

and

 

annelids

[55,56,80,81,100,
241–243]

Induction

 

of

 

limb

 

and

 

spinal

 

cord

regeneration

Amphibia

 

[244–246]

Control

 

of

 

gene

 

expression

 

and

anatomy

 

in

 

craniofacial

patterning

Xenopus

 

embryo

 

[247]

Induction

 

of

 

eye

 

development

 

Xenopus

 

embryo

 

[248]

main

 

classes

 

of

 

components

 

(Fig.

 

2B):

 

a)

 

ion

 

channel

 

and

 

pump

proteins

 

that

 

set

 

resting

 

potential

 

(Vmem),

 

b)

 

gap

 

junction

 

proteins

(such

 

as

 

Connexins

 

or

 

Innexins)

 

which

 

form

 

cytosolic

 

connections

to

 

share

 

a

 

cell’s

 

electrical

 

and

 

chemical

 

state

 

with

 

neighboring

cells,

 

c)

 

neurotransmitters

 

and

 

other

 

small

 

signaling

 

molecules

that

 

move

 

(by

 

electrophoresis

 

or

 

voltage-powered

 

transporters)

across

 

cell

 

groups,

 

and

 

d)

 

transduction

 

machinery

 

that

 

converts

changes

 

in

 

resting

 

potential

 

to

 

downstream

 

processes

 

such

 

as

 

tran-

scriptional

 

changes

 

(see

 

[58]

 

for

 

review).

 

Advances

 

in

 

imaging

endogenous

 

anatomical

 

gradients

 

of

 

resting

 

potential

 

across

 

tis-

sues

 

in

 

vivo

 

[85–87],

 

and

 

the

 

development

 

of

 

techniques

 

for

 

specific

modification

 

of

 

endogenous

 

bioelectric

 

aspects

 

of

 

morphogenesis

[88,89]

 

have

 

uncovered

 

roles

 

for

 

bioelectric

 

prepatterns

 

(Fig.

 

2B’)

in

 

morphogenesis

 

across

 

phyla,

 

including

 

the

 

alignment

 

of

 

the

 

left-

right

 

axis

 

[90]

 

(sea

 

urchin,

 

frog,

 

chick),

 

control

 

of

 

developing

 

organ

size

 

and

 

shape

 

[91–93]

 

(Drosophila,

 

frog,

 

and

 

zebrafish),

 

induction

of

 

appendage

 

regeneration

 

[94]

 

(frog),

 

and

 

craniofacial

 

patterning

[95,96]

 

(frog,

 

mouse,

 

and

 

human).

Bioelectric

 

pathways

 

are

 

most

 

efficiently

 

probed

 

by

 

mis-

expressing

 

dominant

 

channels,

 

including

 

optogenetic

 

actuators

[95,97–99];

 

however,

 

the

 

expression

 

of

 

exogenous

 

DNA

 

is

 

not

 

yet

possible

 

in

 

planaria

 

(it

 

is

 

not

 

yet

 

known

 

if

 

this

 

is

 

related

 

to

 

the

 

lack

 

of

genetic

 

mutant

 

lines

 

in

 

planaria).

 

Instead,

 

loss-of-function

 

via

 

RNAi

or

 

small

 

molecule

 

activators/inhibitors

 

(which

 

also

 

offer

 

the

 

ben-

efit

 

of

 

mass

 

spectrometry-verified

 

wash-out

 

experiments)

 

can

 

be

used

 

to

 

make

 

predictable

 

changes

 

in

 

resting

 

potential

 

patterns

 

and

downstream

 

outcomes.

 

Targeting

 

native

 

bioelectric

 

components

in

 

planaria

 

has

 

revealed

 

a

 

dependence

 

of

 

the

 

head-tail

 

decision

on

 

voltage

 

gradients

 

driven

 

by

 

the

 

H+/K+-ATPase

 

[100];

 

alteration

of

 

the

 

normal

 

bioelectric

 

gradient

 

in

 

regenerative

 

fragments

 

[100]

(using

 

RNAi

 

or

 

drug

 

inhibitors)

 

can

 

produce

 

double-head

 

or

 

no-

head

 

heteromorphoses

 

(Fig.

 

3

 

A–A”)

 

in

 

D.

 

japonica.

 

Similar

 

work

 

in

 

S.

mediterranea

 

identified

 

a

 

role

 

for

 

bioelectric

 

signaling

 

in

 

size

 

control

and

 

rescaling

 

of

 

the

 

head

 

[101],

 

a

 

function

 

that

 

appears

 

conserved

 

in

brain

 

[91],

 

eye

 

[102],

 

and

 

tail

 

[92].

 

In

 

addition

 

to

 

proteins

 

that

 

gen-

erate

 

electrical

 

gradients,

 

gap

 

junctions

 

(a.k.a.

 

electrical

 

synapses)

are

 

crucial

 

to

 

the

 

function

 

of

 

networks

 

because

 

they

 

are

 

highly

 

con-

trollable

 

valves

 

[103],

 

which

 

cells

 

can

 

use

 

to

 

regulate

 

the

 

spatial

propagation

 

of

 

electrochemical

 

signaling

 

through

 

tissues

 

[104].

 

It

is

 

thus

 

no

 

surprise

 

that

 

both

 

RNAi-based

 

and

 

pharmacological

 

tar-

geting

 

of

 

innexin-based

 

gap

 

junctions

 

revealed

 

roles

 

in

 

the

 

control

of

 

stem

 

cell

 

dynamics

 

[105]

 

and

 

head/tail

 

polarity

 

[106].

2.2.

 

Prediction

 

2:

 

neurotransmitters

 

are

 

involved

 

in

 

planarian

patterning

 

control

A

 

key

 

component

 

to

 

the

 

function

 

of

 

neural

 

circuits

 

are

 

neuro-

transmitters

 

 

small

 

signaling

 

molecules

 

that

 

move

 

as

 

a

 

result

 

of

bioelectric

 

dynamics.

 

These

 

are

 

evolutionarily

 

ancient

 

[107],

 

long

predating

 

the

 

appearance

 

of

 

nervous

 

systems,

 

and

 

are

 

known

 

to

be

 

utilized

 

as

 

a

 

kind

 

of

 

morphogen

 

in

 

development,

 

for

 

example

 

in

left-right

 

axial

 

polarity

 

[108,109]

 

and

 

early

 

blastomere

 

dynamics

[110].

 

Are

 

neurotransmitters

 

important

 

for

 

planarian

 

regenera-

tive

 

control

 

[111]?

 

The

 

Marchant

 

laboratory’s

 

elegant

 

use

 

of

 

a

combination

 

of

 

pharmacology

 

and

 

molecular

 

genetics

 

identified

important

 

functional

 

roles

 

for

 

serotonin,

 

voltage-gated

 

calcium

channels,

 

and

 

other

 

neurotransmitter

 

machinery

 

in

 

determining

anterior-posterior

 

polarity

 

in

 

planarian

 

regeneration

 

[112–118].

Unlike

 

RNA

 

or

 

protein,

 

neurotransmitters

 

are

 

too

 

small

 

to

 

effec-

tively

 

tag,

 

hindering

 

efforts

 

to

 

observe

 

their

 

movement

 

through

tissues

 

during

 

regeneration.

 

Recent

 

development

 

of

 

fluorescent

sensors

 

of

 

neurotransmitters

 

[119,120]

 

and

 

light-based

 

techniques

for

 

precise

 

spatio-temporal

 

control

 

of

 

neurotransmitter

 

signaling

[121,122]

 

will

 

revolutionize

 

this

 

area,

 

as

 

soon

 

as

 

misexpression

 

of

exogenous

 

proteins

 

is

 

available

 

in

 

planaria.

2.3.

 

Prediction

 

3:

 

anatomical

 

outcome

 

and

 

genetic

 

default

 

can

diverge

Another

 

feature

 

of

 

bioelectric

 

pathways

 

is

 

that

 

they

 

confer

 

a

degree

 

of

 

robustness

 

and

 

plasticity

 

that

 

enable

 

outcomes

 

diverg-

ing

 

from

 

genetic

 

default.

 

For

 

example,

 

in

 

Xenopus,

 

metastatic

melanoma

 

can

 

be

 

initiated

 

on

 

a

 

background

 

of

 

a

 

perfectly

 

nor-

mal

 

genome

 

(no

 

carcinogens,

 

no

 

oncogenic

 

mutagenesis)

 

[123],

while

 

tumors

 

induced

 

by

 

KRAS

 

mutation

 

can

 

be

 

normalized

 

[97],

and

 

brain

 

defects

 

induced

 

by

 

dominant

 

Notch

 

mutations

 

can

 

be

circumvented

 

[91],

 

all

 

by

 

the

 

appropriate

 

modulation

 

of

 

native

bioelectrical

 

communication

 

among

 

cells.

Large-scale

 

anatomical

 

pattern

 

(the

 

species-specific

 

target

 

mor-

phology)

 

is

 

thought

 

to

 

be

 

determined

 

by

 

the

 

genomic

 

sequence

and

 

its

 

chromatin

 

modifications.

 

Recent

 

data,

 

however,

 

suggest

 

that

bioelectric

 

networks

 

also

 

store

 

key

 

components

 

of

 

this

 

information,

as

 

another

 

epigenetic

 

layer

 

of

 

pattern

 

control

 

[52].

 

This

 

is

 

con-

sistent

 

with

 

Waddington’s

 

original

 

sense

 

of

 

the

 

word

 

epigenetics,

which

 

included

 

much

 

more

 

than

 

chromatin

 

modifications.

 

G.

 

doro-

tocephala

 

planarians

 

regenerate

 

their

 

specific

 

head

 

morphology

with

 

100%

 

fidelity

 

under

 

normal

 

conditions.

 

However

 

(Fig.

 

3B,B’),

when

 

their

 

heads

 

were

 

amputated,

 

and

 

the

 

fragments

 

exposed

 

to

a

 

reagent

 

that

 

alters

 

the

 

bioelectrical

 

connectivity

 

among

 

the

 

cells

[124],

 

they

 

regenerated

 

heads

 

that

 

closely

 

resembled

 

those

 

of

 

sev-

eral

 

other

 

species

 

(as

 

confirmed

 

by

 

quantitative

 

morphometrics).

Moreover,

 

it

 

was

 

not

 

only

 

external

 

head

 

shapes

 

that

 

were

 

con-

verted,

 

but

 

also

 

the

 

shape

 

of

 

the

 

brain

 

and

 

distribution

 

of

 

neoblasts

in

 

the

 

head

 

became

 

similar

 

to

 

that

 

observed

 

in

 

the

 

heads

 

of

 

these

other

 

extant

 

species

 

[124].

One

 

notable

 

aspect

 

is

 

that

 

the

 

choice

 

of

 

heads

 

was

 

stochastic

(Fig.

 

3C)

 

 

the

 

different

 

types

 

of

 

heads

 

appeared

 

in

 

the

 

same

 

cohort

of

 

animals

 

treated

 

identically

 

and

 

raised

 

in

 

the

 

same

 

dish,

 

but

 

in

frequencies

 

proportional

 

to

 

the

 

evolutionary

 

distance

 

between

 

the

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

131

Fig.

 

3.

 

Bioelectrically-mediated

 

changes

 

of

 

patterning

 

in

 

planaria.

(A)

 

D.

 

japonica

 

mid-fragments

 

exhibit

 

bioelectric

 

gradients,

 

with

 

anterior

 

ends’

 

cellular

 

Vmem depolarized

 

compared

 

to

 

those

 

of

 

posterior

 

cells.

 

This

 

pattern

 

can

 

be

 

detected

 

[85]

via

 

voltage-sensitive

 

fluorescent

 

dyes

 

(A’),

 

and

 

modified

 

with

 

ion

 

channel

 

drugs,

 

which

 

alter

 

the

 

endogenous

 

bioelectrical

 

gradient

 

toward

 

bi-

 

or

 

no-head

 

heteromorphoses

respectively

 

(A”),

 

demonstrating

 

that

 

the

 

bioelectric

 

pattern

 

is

 

instructive

 

for

 

large-scale

 

anatomical

 

polarity

 

along

 

the

 

AP

 

axis

 

[100,132].

(B)

 

G.

 

dorotocephala

 

planaria

 

exhibit

 

a

 

characteristically

 

distinct

 

head

 

shape,

 

compared

 

to

 

other

 

species.

 

(B’)

 

When

 

fragments

 

of

 

G.

 

dorotocephala

 

were

 

briefly

 

treated

 

with

 

a

gap

 

junction

 

blocker

 

[124],

 

they

 

regenerated

 

heads

 

whose

 

shapes

 

matched

 

those

 

of

 

other

 

extant

 

species

 

of

 

planaria.

(C)

 

The

 

appearance

 

of

 

these

 

head

 

shapes

 

in

 

a

 

single

 

cohort

 

of

 

worms

 

treated

 

together

 

was

 

stochastic,

 

appearing

 

at

 

frequencies

 

proportional

 

to

 

the

 

evolutionary

 

distance

between

 

the

 

species.

Panel

 

A’

 

was

 

created

 

by

 

Taisaku

 

Nogi.

 

Panels

 

B–C

 

used

 

with

 

permission

 

from

 

[124].

914327a74b2eb3aa0c9c512d8ed73e06-html.html

132

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

Fig.

 

4.

 

Permanent

 

change

 

to

 

planarian

 

target

 

morphology:

 

resetting

 

bioelectrical

 

pattern

 

memories.

(A)

 

Exposing

 

planarian

 

fragments

 

to

 

the

 

gap

 

junction

 

blocker

 

1-octanol

 

for

 

three

 

days

 

results

 

in

 

a

 

permanent

 

re-setting

 

of

 

the

 

target

 

morphology

 

[193].

 

Trunk

 

fragments

 

of

such

 

worms

 

continue

 

to

 

regenerate

 

as

 

double-headed

 

animals

 

in

 

perpetuity,

 

in

 

plain

 

water.

 

1-octanol

 

is

 

washed

 

out

 

of

 

the

 

worm

 

tissues

 

within

 

2–3

 

days

 

(as

 

shown

 

by

 

mass

spectrometry);

 

this

 

demonstrates

 

that

 

transient

 

physiological

 

changes

 

become

 

consolidated

 

as

 

long-term

 

pattern

 

memory,

 

without

 

genomic

 

editing.

 

The

 

animals’

 

target

morphology

 

can

 

be

 

re-set

 

back

 

to

 

normal

 

by

 

altering

 

the

 

bioelectric

 

circuit

 

back

 

to

 

a

 

wild-type

 

distribution,

 

using

 

ion

 

pump

 

drugs

 

such

 

as

 

SCH28080

 

(A’).

(B)

 

Animals

 

that

 

did

 

not

 

become

 

double-headed

 

after

 

an

 

initial

 

exposure

 

to

 

1-octanol

 

are

 

not

 

wild-type

 

because

 

when

 

cut

 

in

 

plain

 

water

 

they

 

give

 

rise

 

to

 

the

 

same

 

percentage

of

 

double-headed

 

worms

 

in

 

each

 

generation.

 

Here

 

shown

 

as

 

a

 

state

 

transition

 

diagram

 

with

 

double-headed

 

worms

 

always

 

regenerating

 

as

 

double-headed

 

(a

 

terminal

 

state)

while

 

cryptics

 

continue

 

to

 

generate

 

double-headed

 

worms

 

at

 

the

 

same

 

ratio

 

(each

 

arrow

 

is

 

labeled

 

with

 

the

 

reagent

 

applied

 

at

 

regeneration

 

and

 

the

 

percentage

 

of

 

outcomes).

(B’)

 

Cryptic

 

worms

 

are

 

identical

 

to

 

wild-type

 

worms

 

in

 

their

 

anatomy,

 

expression

 

of

 

head

 

and

 

tail

 

marker,

 

and

 

stem

 

cell

 

distribution

 

[132].

 

However,

 

a

 

uniform

 

depolarization

of

 

endogenous

 

bioelectrical

 

gradient

 

reveals

 

the

 

difference

 

between

 

normal

 

and

 

cryptic

 

animals.

 

This

 

altered

 

bioelectric

 

distribution

 

is

 

the

 

key

 

functional

 

component

 

of

 

the

re-writable

 

pattern

 

memory

 

mediating

 

the

 

regenerative

 

control.

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

133

Table

 

3

Ion

 

channels

 

and

 

pumps

 

proteins

 

implicated

 

in

 

patterning

 

by

 

genetic

 

screens.

Protein

 

Morphogenetic

 

role

 

or

 

LOF

 

phenotype

 

Species

 

References

TRH1

 

K+ transporter

 

Root

 

hair

 

patterning

 

Arabidopsis

 

[249]

Kir2.1

 

K+ channel

Wing

 

patterning

Drosophila

 

[250]

Kir7.1

 

K+ channel

 

Craniofacial

 

patterning,

 

lung

 

development

 

Mouse

 

[251]

NHE2

 

Na+/H + exchanger

 

Epithelial

 

patterning

 

Drosophila

 

[252]

V-ATPase

 

proton

 

pump

Wing

 

hair

 

patterning,

 

Pigmentation

 

and

 

brain

patterning

Drosophila

 

[253,254]

Craniofacial

 

patterning

 

Medaka,

 

Human

 

[255]

HCN1,

 

Kv3.1

 

K + channels

 

Forebrain

 

patterning

 

Mouse

 

[256,257]

KCNC1

 

K + channel

Growth

 

deficits

Mouse

 

[256]

TWIK-1

 

K + channel

 

(KCNK1)

 

Cardiac

 

(atrial)

 

size

 

Mouse

 

[258]

KCNJ6

 

K + channel

 

Keppen-Lubinsky

 

syndrome

 

 

craniofacial

 

and

brain

Human

 

[96]

KCNH1

 

(hEAG1)

 

K + channel

 

and

 

ATP6V1B2

V-ATPase

 

proton

 

pump

Zimmermman-Laband

 

and

 

Temple-Baraitser

syndrome

 

 

craniofacial

 

and

 

brain

 

defects,

dysplasia/aplasia

 

of

 

nails

 

of

 

thumb

 

and

 

great

toe.

Human

 

[259,260]

GLRa4

 

chloride

 

channel

 

Craniofacial

 

anomalies

 

Human

 

[261]

KCNA1

 

K+ channel

Megencephaly

 

Mouse

 

[262]

NCX-9

 

(Na+/Ca2+)

 

exchanger

 

Neural

 

patterning

 

C.

 

elegans

 

[263]

GLRa4

 

chloride

 

channel

 

Craniofacial

 

anomalies

 

Human

 

[261]

KCNJ8

 

K+

 

Cantu

 

syndrome

 

 

face,

 

heart,

 

skeleton,

 

and

brain

 

defects

Human

 

[264–266]

NALCN

 

(Na + leak

 

channel)

 

Freeman-Sheldon

 

syndrome

 

 

limbs,

 

face,

brain

Human

 

[267]

CFTR

 

chloride

 

channel

Bilateral

 

absence

 

of

 

vas

 

deferens

Human

 

[268,269]

KCNK9,

 

TASK3

 

K + channels

 

Birk-Barel

 

Dysmorphism

 

Syndrome

 

craniofacial

 

defects

Human

 

[270,271]

Kir6.2

 

K + channel

 

Craniofacial

 

defects

 

Human

 

[272]

KCNQ1

 

K

 

+

 

channel

 

(via

 

epigenetic

regulation)

Hypertrophy

 

of

 

tongue,

 

liver,

 

spleen,

 

pancreas,

kidneys,

 

adrenals,

 

genitalia

 

Beckwith-Wiedemann

 

syndrome;

 

craniofacial

and

 

limb

 

defects

Human,

 

Mouse

 

[273–275]

KCNQ1

 

K + channel

 

Jervell

 

and

 

Lange-Nielsen

 

syndrome

 

-

 

inner

 

ear

and

 

limb

Human,

 

mouse

 

[276–278]

Kir2.1

 

K + channel

 

(KNCJ2)

 

Andersen-Tawil

 

syndrome

 

 

craniofacial,

 

limb,

ribs

Human,

 

mouse

 

[250,279,280]

GABA-A

 

receptor

 

(chloride

 

channel)

Angelman

 

Syndrome

 

-

 

craniofacial

 

(e.g.,

 

cleft

palate)

 

and

 

hand

 

patterning

Human,

 

mouse

 

[281–283]

TMEM16A

 

chloride

 

channel

 

Tracheal

 

morphogenesis

 

Mouse

 

[284]

Girk2

 

K + channel

 

Cerebellar

 

development

 

defects

 

Mouse

 

[285–288]

KCNH2

 

K + channel

 

Cardiac,

 

craniofacial

 

patterning

 

defects

 

Mouse

 

[289]

KCNQ1

 

K + channel

Abnormalities

 

of

 

rectum,

 

pancreas,

 

and

stomach

Mouse

 

[290]

NaV1.2

 

Muscle

 

and

 

nerve

 

repair

 

defects

 

Xenopus

 

[94]

Kir6.1

 

K + channel

 

Eye

 

patterning

 

defects

 

Xenopus

 

[248]

V-ATPase

 

ion

 

pump

 

Left-right

 

asymmetry

 

defects,

 

muscle

 

and

nerve

 

repair

Xenopus,

 

chick,

 

zebrafish

 

[237,291]

H,K-ATPase

 

ion

 

pump

 

Left-right

 

asymmetry

 

defects

 

Xenopus,

 

sea

 

urchin

 

[90,292,293]

Kir7.1

 

K + channel

 

Melanosome

 

development

 

defects

 

Zebrafish

 

[294]

Kv

 

channels

 

Fin

 

size

 

regulation,

 

heart

 

size

 

regulation

 

Zebrafish,

 

mouse

 

[92,295]

NaV

 

1.5,

 

Na+/K+-ATPase

 

Cardiac

 

morphogenesis

 

Zebrafish

 

[296,297]

species

 

they

 

resembled

 

[124].

 

These

 

data

 

showed

 

that

 

a

 

wild-type

animal

 

exposed

 

briefly

 

to

 

a

 

non-mutagenic

 

modulator

 

of

 

gap

 

junc-

tions

 

can

 

generate

 

heads

 

belonging

 

to

 

species

 

∼150

 

million

 

years

distant.

 

This

 

drastic

 

effect

 

of

 

a

 

transient

 

change

 

to

 

bioelectric

 

cir-

cuit

 

dynamics,

 

suggests

 

these

 

as

 

a

 

novel

 

form

 

of

 

epigenetics,

 

and

one

 

with

 

convenient

 

master-regulator

 

properties

 

that

 

could

 

read-

ily

 

have

 

been

 

exploited

 

by

 

evolution

 

(e.g.,

 

via

 

mutations

 

in

 

ion

channel

 

coding

 

or

 

promoter

 

regions)

 

as

 

part

 

of

 

the

 

exploration

of

 

a

 

morphospace.

 

Another

 

notable

 

aspect

 

is

 

that

 

the

 

change

 

was

not

 

permanent:

 

several

 

weeks

 

after

 

completion

 

of

 

head

 

construc-

tion,

 

remodeling

 

suddenly

 

began,

 

and

 

converted

 

the

 

heads

 

back

to

 

a

 

normal

 

G.

 

dorotocephala

 

shape.

 

Unlike

 

the

 

example

 

of

 

perma-

nent

 

patterning

 

change

 

described

 

below,

 

this

 

head-shape

 

switch

most

 

resembles

 

short-term

 

memory

 

without

 

consolidation.

 

Work

is

 

currently

 

on-going

 

to

 

formulate

 

and

 

test

 

quantitative

 

dynami-

cal

 

systems

 

models

 

to

 

reveal

 

why

 

head

 

shape

 

changes

 

represent

shallower

 

attractor

 

states

 

than

 

head-tail

 

decisions

 

in

 

the

 

physico-

chemical

 

state

 

space

 

of

 

the

 

circuits

 

driving

 

regeneration

 

in

 

planaria

(thus,

 

easier

 

for

 

the

 

system

 

to

 

escape

 

from)

 

[61,125].

2.4.

 

Prediction

 

4:

 

pattern

 

memory

 

can

 

be

 

over-written

The

 

dominant

 

paradigm

 

for

 

regeneration

 

is

 

that

 

of

 

emergence,

with

 

cells

 

behaving

 

according

 

to

 

specific

 

rules,

 

and

 

the

 

combination

of

 

a

 

large

 

number

 

of

 

these

 

individual

 

activities

 

somehow

 

resulting

in

 

the

 

same

 

complex

 

body

 

being

 

created

 

from

 

diverse

 

starting

 

con-

ditions.

 

A

 

complementary

 

view

 

is

 

that

 

a

 

specific

 

pattern

 

memory

(the

 

organism’s

 

target

 

morphology)

 

is

 

encoded,

 

at

 

least

 

in

 

very

 

gen-

(C)

 

The

 

same

 

body

 

can

 

contain

 

at

 

least

 

two

 

diverse

 

bioelectric

 

patterns

 

guiding

 

future

 

growth:

 

wild-type

 

(permanent

 

single-headed)

 

or

 

cryptic

 

(destabilized,

 

stochastic).

 

One

way

 

to

 

understand

 

stable,

 

discrete

 

anatomical

 

outcomes

 

emerging

 

from

 

bioelectric

 

circuit

 

activity

 

is

 

as

 

stable

 

attractors

 

in

 

a

 

morphospace

 

defined

 

by

 

the

 

voltage

 

states

 

of

the

 

two

 

ends

 

of

 

the

 

body.

Panel

 

C

 

made

 

by

 

Jeremy

 

Guay

 

of

 

Peregrine

 

Creative.

914327a74b2eb3aa0c9c512d8ed73e06-html.html

134

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

eral

 

terms,

 

by

 

some

 

physical

 

mechanism

 

in

 

tissue

 

that

 

is

 

read

 

out

and

 

elaborated

 

during

 

regeneration

 

and

 

regulative

 

embryogene-

sis

 

(and

 

serves

 

as

 

reference

 

for

 

the

 

stopping

 

point

 

for

 

new

 

growth

and

 

remodeling).

 

Neuroscientists

 

are

 

comfortable

 

with

 

cellular

 

net-

works

 

that

 

guide

 

flexible

 

activity

 

to

 

achieve

 

stored

 

goal

 

states.

Could

 

a

 

more

 

ancient

 

version

 

of

 

this

 

system

 

be

 

more

 

widely

 

utilized

for

 

pattern

 

control

 

in

 

biology?

 

One

 

of

 

the

 

major

 

benefits

 

emerg-

ing

 

from

 

cybernetics

 

and

 

control

 

theory

 

over

 

the

 

last

 

six

 

decades

is

 

a

 

solid

 

grounding

 

for

 

teleological-seeming

 

processes

 

in

 

rigor-

ous

 

engineering

 

principles.

 

Systems

 

that

 

implement

 

specific

 

goal

states

 

can

 

be

 

modeled

 

as

 

homeostatic

 

processes

 

that

 

do

 

not

 

require

any

 

anthropomorphisms,

 

and

 

are

 

routinely

 

constructed

 

(from

 

ther-

mostats

 

to

 

self-driving

 

cars).

 

Thus,

 

one

 

way

 

to

 

model

 

regeneration

is

 

as

 

a

 

kind

 

of

 

pattern

 

homeostasis

 

 

a

 

TOTE

 

(Test-Operate-Test-

Exit)

 

loop

 

[39,126].

 

Planaria

 

tissues

 

can

 

be

 

envisioned

 

as

 

executing

a

 

continuous

 

error

 

minimization,

 

striving

 

to

 

reduce

 

the

 

difference

between

 

the

 

current

 

morphology

 

and

 

the

 

species’

 

target

 

morphol-

ogy

 

(a

 

kind

 

of

 

least-action

 

model,

 

as

 

is

 

often

 

used

 

to

 

understand

the

 

role

 

of

 

other

 

physical

 

forces

 

in

 

morphogenesis

 

[127–129]).

 

A

key

 

aspect

 

of

 

any

 

homeostatic

 

process

 

is

 

that

 

it

 

has

 

to

 

store

 

a

 

set-

point

 

 

planarian

 

tissue

 

would

 

have

 

to

 

represent

 

(encode)

 

some

amount

 

of

 

information

 

about

 

the

 

bodyplan

 

which

 

must

 

be

 

regen-

erated

 

to

 

(and

 

stopped

 

when

 

the

 

current

 

anatomy

 

matches

 

this

pattern

 

memory).

 

Thus,

 

one

 

prediction

 

of

 

this

 

highly

 

speculative

viewpoint

 

is

 

that

 

it

 

should

 

be

 

possible

 

to

 

over-write

 

the

 

setpoint

and

 

permanently

 

change

 

the

 

shape

 

to

 

which

 

the

 

animals

 

regenerate

in

 

the

 

future.

Precisely

 

this

 

was

 

discovered

 

when

 

D.

 

japonica

 

animals

 

were

treated

 

with

 

1-octanol

 

(Fig.

 

4A)

 

 

an

 

experiment

 

motivated

 

by

 

the

fact

 

that

 

gap

 

junctions

 

are

 

a

 

key

 

component

 

of

 

memory

 

in

 

the

brain

 

and

 

also

 

an

 

ideal

 

candidate

 

for

 

the

 

long-range

 

communica-

tion

 

between

 

the

 

wound

 

site

 

and

 

remote

 

tissues.

 

The

 

result

 

was

double-headed

 

bipolar

 

heteromorphoses

 

[106],

 

which,

 

remark-

ably,

 

continued

 

to

 

regenerate

 

as

 

double-headed

 

in

 

perpetuity

 

with

no

 

further

 

treatments

 

(Fig.

 

4A’).

 

These

 

animals,

 

which

 

lose

 

all

trace

 

of

 

the

 

gap

 

junction-blocking

 

reagent

 

in

 

a

 

few

 

days

 

after

initial

 

treatment,

 

are

 

permanently

 

converted

 

to

 

regenerating

 

a

different

 

target

 

morphology

 

by

 

a

 

transient

 

physiological

 

perturba-

tion.

 

This

 

phenomenon

 

was

 

first

 

described

 

as

 

“trophic

 

memory”

in

 

Bubenik’s

 

work

 

on

 

deer

 

antler

 

injuries

 

and

 

pattern

 

changes

in

 

subsequent

 

years

 

of

 

regeneration

 

[130];

 

planaria

 

provide

 

the

first

 

molecularly-tractable

 

model

 

system

 

in

 

which

 

this

 

fascinating

aspect

 

of

 

regeneration

 

biology

 

can

 

be

 

studied

 

[131].

 

The

 

permanent

double-head

 

state

 

can

 

be

 

re-set

 

back

 

to

 

a

 

wild-type

 

single-head

target

 

morphology

 

by

 

a

 

different

 

transient

 

modulation

 

of

 

the

 

bio-

electric

 

circuit

 

using

 

the

 

ion

 

pump

 

blocker

 

SCH28080

 

(Fig.

 

4B).

A

 

biophysical

 

model

 

of

 

the

 

planarian

 

pattern

 

memory

 

has

 

been

analyzed

 

[132],

 

explaining

 

how

 

patterning

 

in

 

regenerates

 

can

 

be

templated

 

off

 

of

 

stable

 

biophysical

 

properties

 

of

 

fragments

 

despite

wild-type

 

genomic

 

sequence.

 

While

 

it

 

is

 

very

 

possible

 

that

 

the

permanent

 

change

 

of

 

pattern

 

memory

 

also

 

involves

 

chromatin

modification

 

machinery

 

[133],

 

the

 

field

 

of

 

epigenetics

 

does

 

not

 

yet

offer

 

an

 

explanation

 

of

 

how

 

large-scale

 

anatomical

 

patterning

 

out-

comes

 

would

 

result

 

from

 

specific

 

chromatin

 

states

 

in

 

individual

cells.

 

At

 

the

 

same

 

time,

 

the

 

long-range

 

organizing

 

properties

 

of

electric

 

fields

 

arising

 

from

 

ion

 

channel

 

activity

 

provide

 

a

 

natural

medium

 

in

 

which

 

to

 

understand

 

these

 

questions

 

of

 

how

 

large-

scale

 

order

 

arises

 

[125,134–136].

 

The

 

interaction

 

of

 

bioelectrics

 

and

chromatin

 

modification

 

machinery

 

[137,138]

 

remains

 

therefore

 

an

important

 

area

 

for

 

future

 

work.

As

 

with

 

any

 

intervention

 

(molecular-genetic

 

or

 

pharmacolog-

ical),

 

the

 

effects

 

of

 

gap

 

junction

 

inhibiting

 

reagents

 

are

 

not

 

100%

penetrant

 

in

 

a

 

cohort

 

of

 

planaria

 

treated

 

together.

 

However,

 

an

interesting

 

phenomenon

 

was

 

discovered

 

when

 

the

 

unaffected

“escapees”,

 

which

 

looked

 

like

 

normal

 

one-headed

 

planaria,

 

were

re-cut

 

weeks

 

after

 

the

 

initial

 

treatment

 

without

 

any

 

further

 

inter-

ventions.

 

It

 

was

 

found

 

[132]

 

that

 

these

 

animals

 

were

 

in

 

fact

 

not

wild-type:

 

they

 

differed

 

from

 

normal

 

planaria

 

in

 

that

 

when

 

cut,

 

in

plain

 

water,

 

they

 

generated

 

double-headed

 

worms

 

in

 

the

 

same

 

pro-

portion

 

as

 

the

 

original

 

treated

 

cohort

 

(Fig.

 

4B).

 

The

 

same

 

is

 

true

 

for

subsequent

 

generations

 

from

 

these

 

“cryptic”

 

worms.

 

The

 

cryptic

worms

 

were

 

analyzed

 

and

 

shown

 

to

 

have

 

wild-type

 

anatomy,

 

his-

tology,

 

stem

 

cell

 

distribution,

 

and

 

expression

 

of

 

several

 

head

 

and

tail

 

markers

 

(Fig.

 

4B’).

 

What

 

makes

 

these

 

worms,

 

bearing

 

appar-

ently

 

normal

 

hardware,

 

regenerate

 

in

 

a

 

stochastic

 

manner

 

in

 

plain

water?

 

It

 

is

 

that

 

they

 

bear

 

an

 

aberrant

 

pattern

 

memory:

 

voltage-

sensitive

 

fluorescent

 

dyes

 

reveal

 

that

 

their

 

tissues

 

are

 

uniformly

more

 

depolarized

 

than

 

wild-type

 

worms

 

(Fig.

 

4B’).

Numerous

 

examples

 

in

 

engineering

 

(e.g.,

 

flip

 

flops)

 

exist

 

of

 

how

the

 

same

 

electrical

 

circuit

 

can

 

be

 

bi-stable,

 

able

 

to

 

store

 

several

discrete

 

possible

 

patterns

 

of

 

gene

 

expression

 

or

 

ion

 

flow

 

[139].

 

It

is

 

not

 

yet

 

known

 

whether

 

these

 

concepts

 

will

 

directly

 

translate

to

 

understanding

 

planarian

 

pattern

 

control;

 

however,

 

the

 

cryptic

worm

 

data

 

described

 

above

 

provide

 

a

 

simple

 

illustration

 

of

 

how

 

an

anatomically-normal

 

body

 

can

 

support

 

distinct

 

bodyplan

 

encod-

ings

 

that

 

are

 

latent,

 

but

 

recalled

 

if

 

the

 

animal

 

is

 

challenged

 

to

regenerate

 

in

 

the

 

future.

 

In

 

this,

 

they

 

fulfill

 

the

 

basic

 

properties

 

that

define

 

memory

 

(whether

 

neural,

 

electronic,

 

or

 

molecular):

 

encod-

ing

 

discrete

 

large-scale

 

outcomes,

 

which

 

are

 

long-term

 

stable,

 

and

yet

 

sufficiently

 

labile

 

to

 

be

 

able

 

to

 

be

 

re-written

 

by

 

appropriate

stimuli.

 

A

 

dynamical

 

systems

 

perspective

 

on

 

such

 

discrete,

 

stable,

yet

 

potentially

 

labile

 

end-states

 

is

 

to

 

view

 

them

 

as

 

attractors

 

in

the

 

state

 

space

 

of

 

the

 

relevant

 

circuits,

 

which

 

demarcate

 

regions

 

of

morphospace

 

corresponding

 

to

 

different

 

bodyplan

 

layouts

 

(Fig.

 

4C).

2.5.

 

Summary:

 

physiological

 

controls

 

of

 

growth

 

and

 

form

The

 

planarian

 

model

 

system

 

offers

 

experimentally-tractable

examples

 

of

 

rewriting

 

the

 

anatomical

 

setpoint

 

for

 

regeneration

without

 

genomic

 

editing:

 

permanent

 

changes

 

of

 

the

 

pattern

 

to

which

 

animals

 

regenerate

 

following

 

future

 

injury,

 

driven

 

by

 

tran-

sient

 

alterations

 

of

 

physiological

 

state

 

(Fig.

 

4A,

 

B–B’).

 

Currently,

 

our

ability

 

to

 

control

 

biological

 

patterns

 

is

 

limited,

 

and

 

the

 

full

 

range

 

of

possible

 

patterns

 

is

 

unknown.

 

Future

 

work

 

must

 

focus

 

on

 

a

 

better

understanding

 

of

 

the

 

interplay

 

of

 

transcriptional,

 

chromatin-based,

and

 

physiological

 

layers

 

to

 

explain

 

stochasticity

 

in

 

large-scale

anatomical

 

outcomes

 

and

 

long-term

 

stability

 

of

 

target

 

morphology.

The

 

key

 

challenge

 

is

 

to

 

convert

 

pathway

 

and

 

physiological

 

circuit

information

 

at

 

the

 

level

 

of

 

single

 

cells

 

into

 

an

 

understanding

 

of

stable

 

large-scale

 

anatomical

 

attractor

 

states,

 

and

 

to

 

achieve

 

a

 

sys-

tems

 

level

 

understanding

 

of

 

shape

 

homeostasis

 

and

 

regeneration

(Fig.

 

4A).

 

AP

 

polarity

 

can

 

be

 

explained

 

using

 

chemical

 

positional

information

 

gradients

 

coupled

 

with

 

directed

 

transport

 

[125,140];

it

 

remains

 

to

 

be

 

seen

 

whether

 

the

 

full

 

complexity

 

of

 

planarian

 

pat-

tern

 

homeostasis

 

(including

 

shape,

 

cell

 

number

 

and

 

proportion,

 

and

patterning

 

along

 

DV/LR

 

axes)

 

will

 

require

 

connectionist

 

or

 

other

neural-like

 

computational

 

models

 

[40,141].

 

Just

 

how

 

much

 

global

information

 

is

 

encoded

 

in

 

physiological

 

circuits,

 

to

 

what

 

resolution

a

 

target

 

morphology

 

might

 

be

 

represented

 

in

 

tissue,

 

the

 

size

 

of

 

the

smallest

 

unit

 

that

 

processes

 

bioelectric

 

states

 

(single

 

cells,

 

or

 

cell

groups),

 

and

 

how

 

much

 

predictive

 

control

 

can

 

be

 

gained

 

over

 

pat-

terning

 

in

 

planaria,

 

are

 

open

 

questions

 

that

 

will

 

require

 

not

 

only

technique

 

development

 

but

 

conceptual

 

advances

 

that

 

may

 

need

 

to

borrow

 

from

 

neuroscience,

 

control

 

theory,

 

and

 

cybernetics.

3.

 

Computational

 

approaches

 

to

 

an

 

integrative

understanding

The

 

mysteries

 

of

 

planarian

 

regeneration

 

have

 

been

 

with

 

us

 

for

∼120

 

years

 

[142],

 

and

 

one

 

of

 

the

 

most

 

challenging

 

aspects

 

has

 

been

the

 

discovery

 

of

 

specific

 

models

 

that

 

exhibit

 

the

 

desired

 

pattern-

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

135

ing

 

properties

 

matching

 

the

 

huge

 

base

 

of

 

functional

 

knowledge

 

in

this

 

model

 

system.

 

As

 

with

 

many

 

difficult

 

problems

 

in

 

science,

this

 

field

 

is

 

ripe

 

for

 

assistance

 

from

 

the

 

revolution

 

in

 

informa-

tion

 

technology.

 

Alongside

 

new

 

recent

 

databases

 

of

 

transcriptomic,

phylogenetic,

 

and

 

biochemical

 

resources

 

(such

 

as

 

PlanNET

 

and

PlanMine)

 

[143,144],

 

two

 

new

 

directions

 

are

 

emerging:

 

simulation

environments

 

for

 

interrogating

 

the

 

complex

 

dynamics

 

of

 

pattern-

ing

 

models,

 

and

 

machine

 

learning

 

tools

 

for

 

helping

 

to

 

derive

 

models

with

 

desired

 

properties.

3.1.

 

Current

 

state

 

of

 

the

 

art

 

in

 

understanding

 

regenerative

dynamics:

 

gradients

 

and

 

beyond

Next,

 

we

 

summarize

 

the

 

current

 

state

 

of

 

the

 

art

 

in

 

computa-

tional

 

understanding

 

of

 

planarian

 

regeneration,

 

focusing

 

primarily

on

 

anterior-posterior

 

axial

 

polarity.

The

 

mechanisms

 

underlying

 

body-plan

 

control

 

in

 

planaria

 

have

been

 

explored

 

from

 

a

 

variety

 

of

 

different

 

perspectives,

 

from

 

bio-

chemical

 

gradients

 

to

 

neural

 

network

 

dynamics

 

[141,145,146].

Models

 

exploring

 

gradients

 

of

 

positional

 

information

 

(mediated

by

 

gene

 

expression

 

and

 

secondary

 

messenger

 

gradients)

 

have

shown

 

particular

 

promise

 

[147–149].

 

The

 

concept

 

of

 

morphogen

gradients

 

underlying

 

control

 

of

 

body

 

plan

 

regulation

 

in

 

planaria

regeneration

 

in

 

a

 

concentration-dependent

 

manner

 

is

 

an

 

old

 

idea

that

 

was

 

first

 

proposed

 

at

 

the

 

turn

 

of

 

the

 

century

 

(reviewed

 

in

[10]).

 

With

 

the

 

advent

 

of

 

molecular

 

genetics

 

and

 

RNAi-mediated

loss-of-function

 

in

 

planaria,

 

recent

 

experiments

 

have

 

revealed

 

the

existence

 

of

 

gene

 

expression

 

and

 

signaling

 

gradients

 

along

 

the

anterior-posterior

 

axis,

 

which

 

have

 

been

 

found

 

to

 

be

 

crucial

 

for

anterior-posterior

 

body-plan

 

control

 

[10,44,150].

 

Most

 

distinctly,

canonical

 

Wnt/

␤-Catenin

 

signaling

 

has

 

been

 

strongly

 

implicated

in

 

posterior

 

development

 

[151,152],

 

via

 

the

 

inhibition

 

of

 

signaling

pathways

 

such

 

as

 

extracellular

 

receptor

 

kinase

 

(ERK)

 

that

 

are

 

asso-

ciated

 

with

 

head

 

development

 

[153–155].

 

Graded

 

Hedgehog

 

(Hh),

fibroblast

 

growth

 

factor

 

receptor

 

like

 

(FGFR),

 

and

 

Notum

 

signal-

ing

 

have

 

also

 

been

 

observed

 

[140,149,156–158].

 

RNAi

 

knockdown

of

 

these

 

factors

 

results

 

in

 

dramatic

 

alteration

 

of

 

the

 

regenerated

planarian

 

body

 

plan,

 

including

 

doubled-headed

 

(RNAi

 

of

 

Wnts),

missing

 

tails

 

(RNAi

 

of

 

Hh),

 

and

 

double-tailed

 

(e.g.

 

RNAi

 

of

 

Notum)

heteromorphoses.

A

 

remarkable

 

feature

 

of

 

morphogen

 

gradients

 

in

 

planaria

 

is

 

that

their

 

polarity

 

in

 

the

 

original

 

organism

 

is

 

spontaneously

 

regenerated

with

 

amputation

 

[159].

 

For

 

example,

 

Notum

 

is

 

detectable

 

at

 

strong

concentrations

 

at

 

the

 

anterior

 

of

 

a

 

whole

 

planaria

 

in

 

homeostasis

(and

 

the

 

opposite

 

is

 

true

 

of

 

Wnt1,

 

which

 

is

 

expressed

 

most

 

strongly

at

 

the

 

posterior).

 

When

 

the

 

animal

 

is

 

cut

 

into

 

fragments,

 

each

 

frag-

ment

 

will

 

spontaneously

 

re-form

 

a

 

concentration

 

gradient

 

in

 

a

 

few

hours,

 

so

 

that

 

Notum

 

is

 

expressed

 

strongly

 

at

 

the

 

fragment

 

end

 

ori-

ented

 

towards

 

the

 

previous

 

head

 

location,

 

even

 

if

 

the

 

fragments

are

 

left

 

adjacent

 

to

 

one

 

another

 

(and

 

vice-versa

 

for

 

Wnt1)

 

[159].

This

 

observation

 

of

 

spontaneously

 

reforming

 

polarized

 

Wnt

 

and

Notum

 

gradients

 

provides

 

strong

 

substantiation

 

for

 

the

 

concept

 

of

morphogen

 

gradients

 

underlying

 

anatomical

 

polarity

 

control.

Several

 

models

 

have

 

been

 

proposed

 

to

 

account

 

for

 

the

 

spon-

taneous

 

emergence

 

of

 

morphogen

 

gradients

 

underlying

 

planaria

body

 

plan

 

control

 

[21].

 

Meinhardt

 

and

 

Gierer

 

proposed

 

a

 

math-

ematical

 

reaction-diffusion

 

model

 

consisting

 

of

 

a

 

self-activating

substance

 

acting

 

over

 

a

 

short

 

range

 

in

 

combination

 

with

 

a

 

long-

range

 

acting

 

inhibitor,

 

which

 

was

 

used

 

to

 

describe

 

the

 

formation

 

of

an

 

emergent

 

␤-Catenin

 

gradient

 

along

 

the

 

anterior-posterior

 

axis

[146,160,161].

 

Working

 

with

 

a

 

different

 

underlying

 

mathematical

premise,

 

Stuckeman

 

et

 

al.

 

proposed

 

two

 

mutually

 

antagonistic

 

sig-

naling

 

circuits

 

 

one

 

for

 

the

 

anterior

 

and

 

one

 

instructive

 

for

 

the

posterior

 

 

which

 

together

 

could

 

function

 

as

 

a

 

molecular

 

switch

 

to

control

 

anterior-posterior

 

polarity

 

[140];

 

mathematical

 

modeling

in

 

a

 

spatialized

 

context

 

would

 

be

 

an

 

important

 

step

 

for

 

future

 

work.

Mutually

 

repressive

 

signals

 

function

 

as

 

a

 

distinctly

 

bimodal

 

system

capable

 

of

 

efficiently

 

switching

 

between

 

one

 

of

 

two

 

states

 

[139],

 

in

this

 

case,

 

between

 

head

 

and

 

tail

 

signaling

 

modalities.

 

In

 

Stucke-

man’s

 

conception,

 

posterior

 

development

 

would

 

be

 

comprised

 

of

Wnt/

␤-Catenin

 

signaling,

 

which

 

is

 

proposed

 

to

 

repress,

 

and

 

to

 

be

in

 

turn

 

repressed

 

by,

 

a

 

second

 

unknown

 

signaling

 

modality

 

crucial

for

 

anterior

 

development

 

[140].

While

 

reaction-diffusion

 

models

 

are

 

valuable

 

in

 

contextualizing

body

 

plan

 

control

 

in

 

regeneration

 

in

 

terms

 

of

 

an

 

experimentally

tractable

 

output,

 

and

 

show

 

promise

 

in

 

explaining

 

positional

 

infor-

mation

 

generation

 

and

 

control

 

in

 

planaria,

 

these

 

models

 

suffer

 

from

a

 

fatal

 

flaw:

 

they

 

are

 

highly

 

dependent

 

on

 

size

 

scale,

 

meaning

 

the

type

 

of

 

pattern

 

resulting

 

from

 

the

 

mechanism

 

is

 

dependent

 

on

 

the

size

 

of

 

the

 

organism

 

[145].

 

For

 

a

 

particular

 

model

 

capable

 

of

 

forming

a

 

gradient

 

on

 

a

 

particular

 

organism

 

size,

 

when

 

the

 

organism

 

is

 

cut

into

 

pieces

 

a

 

gradient

 

may

 

no

 

longer

 

form

 

in

 

these

 

smaller

 

pieces;

or,

 

if

 

the

 

organism

 

grows

 

in

 

size,

 

the

 

simple

 

gradient

 

changes

 

to

 

a

more

 

complex

 

pattern

 

such

 

as

 

a

 

collection

 

of

 

spots

 

or

 

stripes,

 

which

would

 

no

 

longer

 

map

 

to

 

a

 

clean

 

anatomical

 

outcome

 

such

 

as

 

one

head

 

and

 

one

 

tail

 

[145].

 

The

 

scale-dependence

 

of

 

many

 

reaction-

diffusion

 

models

 

was

 

addressed

 

by

 

Werner

 

et

 

al.,

 

who

 

developed,

and

 

evaluated

 

in

 

a

 

one-dimensional

 

model,

 

an

 

elegant

 

regulatory

circuit

 

that

 

is

 

able

 

to

 

rescale

 

a

 

monopolar

 

concentration

 

gradient

virtually

 

independent

 

of

 

size.

An

 

alternative

 

solution

 

to

 

the

 

scale-dependence

 

of

 

traditional

reaction-diffusion

 

schemes

 

is

 

to

 

consider

 

polar

 

transport

 

of

 

gene

products

 

and/or

 

secondary

 

messengers

 

[125].

 

Endogenous

 

bioelec-

tricity,

 

which

 

comprises

 

very

 

strong

 

electric

 

fields

 

active

 

across

 

cell

membranes

 

(

∼1.0

 

×

 

106 V/m)

 

and

 

between

 

gap

 

junction-coupled

cells

 

(

∼1.0

 

×

 

104 V/m),

 

with

 

weaker

 

fields

 

(

∼1.0

 

V/m)

 

in

 

the

 

global

environment

 

[162,163],

 

offers

 

a

 

tractable

 

mechanism

 

through

which

 

electrically

 

charged

 

substances

 

may

 

be

 

subjected

 

to

 

direc-

tional

 

transport.

 

Passage

 

of

 

small,

 

charged

 

signaling

 

molecules

 

such

as

 

ATP4−,

 

or

 

neurotransmitters

 

such

 

as

 

5-HT+,

 

across

 

gap

 

junc-

tions

 

in

 

transmembrane

 

potential

 

(Vmem)

 

gradients

 

is

 

a

 

particularly

promising

 

mechanism

 

that

 

has

 

previously

 

been

 

implicated

 

in

 

the

establishment

 

of

 

developmental

 

left-right

 

asymmetry

 

[164]

 

and

neural

 

pathfinding

 

[165]

 

in

 

vertebrate

 

models.

A

 

simple

 

model

 

of

 

bioelectricity-induced

 

polar

 

transport

 

in

gap

 

junction

 

connected

 

cells

 

was

 

recently

 

reported,

 

and

 

compu-

tationally

 

analyzed

 

in

 

a

 

physiologically

 

realistic

 

tissue

 

context.

 

It

exhibited

 

effective

 

self-assembly

 

and

 

reassembly

 

of

 

highly

 

polar-

ized

 

concentration

 

gradients

 

in

 

whole

 

organisms

 

and

 

cut

 

fragments

of

 

highly

 

diverse

 

sizes

 

for

 

a

 

simple

 

model

 

where

 

an

 

anion

 

moves

between

 

cells

 

in

 

a

 

Vmem gradient

 

that

 

is

 

most

 

depolarized

 

at

the

 

anterior

 

[125].

 

Interestingly,

 

Lange

 

and

 

Steel

 

experimentally

detected

 

a

 

highly

 

negatively

 

charged

 

proteinaceous

 

substance

moving

 

in

 

the

 

posterior

 

direction

 

along

 

the

 

anterior-posterior

 

axis

of

 

planaria,

 

which

 

was

 

furthermore

 

found

 

to

 

inhibit

 

head

 

for-

mation

 

[82,83].

 

These

 

observations

 

are

 

also

 

consistent

 

with

 

early

reports

 

from

 

Marsh

 

and

 

Beams,

 

who

 

were

 

able

 

to

 

switch

 

the

anterior-posterior

 

axis

 

of

 

regenerating

 

planaria

 

fragments

 

using

applied

 

electric

 

fields

 

[56,166].

 

Thus,

 

the

 

electrophoretic

 

move-

ment

 

of

 

signaling

 

molecules

 

in

 

endogenous

 

electric

 

fields

 

provides

a

 

straightforward

 

explanation

 

for

 

the

 

regenerating

 

polarized

 

mor-

phogen

 

gradients

 

of

 

planaria

 

in

 

a

 

manner

 

that

 

is

 

essentially

independent

 

of

 

size

 

scale.

3.2.

 

Advances

 

in

 

modeling

 

and

 

simulation:

 

testing

 

available

models

A

 

mature

 

understanding

 

of

 

patterning

 

requires

 

algorithmic

models,

 

which

 

make

 

each

 

step

 

in

 

the

 

process

 

explicit,

 

require

 

clar-

ification

 

of

 

the

 

mechanisms

 

sufficient

 

for

 

pattern

 

control,

 

enable

testable

 

predictions,

 

and

 

can

 

be

 

inverted

 

to

 

infer

 

specific

 

interven-

tions.

 

As

 

in

 

other

 

areas

 

of

 

developmental

 

biology,

 

recent

 

progress

914327a74b2eb3aa0c9c512d8ed73e06-html.html

136

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

Fig.

 

5.

 

Bioinformatics

 

of

 

shape,

 

applied

 

to

 

planaria.

(A,

 

B)

 

The

 

continued

 

development

 

of

 

knowledge

 

in

 

this

 

field

 

will

 

require

 

computational

 

tools

 

going

 

beyond

 

bioinformatics

 

of

 

genes

 

and

 

proteins,

 

to

 

assist

 

in

 

development

and

 

analysis

 

of

 

models.

 

One

 

effort,

 

PlanForm

 

[169],

 

comprises

 

over

 

1,000

 

experiments

 

from

 

the

 

literature,

 

matching

 

the

 

functional

 

manipulations

 

performed

 

(e.g.,

 

specific

cuts,

 

joins,

 

RNAi,

 

bioelectric

 

change;

 

see

 

B)

 

and

 

the

 

resulting

 

anatomical

 

outcomes

 

represented

 

by

 

a

 

graph

 

notation

 

(B’).

(C)

 

One

 

recent

 

application

 

of

 

artificial

 

intelligence

 

to

 

discovery

 

of

 

regulator

 

pathways

 

[168]

 

used

 

evolutionary

 

selection

 

over

 

a

 

population

 

of

 

biochemical

 

models.

 

Here

shown

 

as

 

the

 

progressive

 

reduction

 

of

 

error

 

in

 

the

 

predictions

 

of

 

top

 

candidate

 

models

 

at

 

each

 

generation.

914327a74b2eb3aa0c9c512d8ed73e06-html.html

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

 

137

has

 

allowed

 

planarian

 

research

 

to

 

move

 

beyond

 

arrow

 

diagrams

 

of

pathways

 

to

 

generative

 

models

 

that

 

highlight

 

the

 

spatio-temporal

dynamics

 

that

 

must

 

be

 

implemented

 

to

 

explain

 

regeneration

[167].

 

This

 

is

 

essential

 

to

 

bridge

 

the

 

well-recognized

 

gulf

 

between

the

 

growing

 

deluge

 

of

 

transcriptomic

 

and

 

genomic

 

data

 

and

 

the

anatomical

 

outcomes

 

reported

 

in

 

the

 

functional

 

literature

 

that

 

we

seek

 

to

 

understand

 

and

 

control.

 

Simulation

 

modules

 

for

 

planaria

include

 

ones

 

that

 

focus

 

on

 

biochemical

 

diffusible

 

factors

 

[168],

and

 

the

 

BETSE

 

modeling

 

environment

 

[134],

 

in

 

which

 

a

 

very

 

rich

set

 

of

 

dynamics

 

(including

 

biochemical

 

signaling

 

as

 

well

 

as

 

bio-

electric/physiological

 

processes),

 

can

 

be

 

explicitly

 

modeled

 

in

 

a

bio-realistic

 

spatialized

 

virtual

 

tissue

 

context

 

[125].

The

 

fields

 

of

 

genetics

 

and

 

cell

 

biology

 

have

 

been

 

revolutionized

by

 

bioinformatics

 

-

 

computational

 

tools

 

that

 

help

 

scientists

 

deal

with

 

high-volume

 

molecular/genetic

 

data.

 

Planarian

 

regeneration

was

 

the

 

domain

 

of

 

some

 

of

 

the

 

first

 

efforts

 

at

 

a

 

new

 

bioinformatics

of

 

shape

 

 

software

 

for

 

extracting

 

control

 

principles

 

of

 

multicellu-

lar

 

anatomical

 

control

 

from

 

functional

 

published

 

data.

 

One

 

aspect

of

 

this

 

effort

 

is

 

formalizing

 

results

 

of

 

functional

 

regenerative

 

exper-

iments

 

and

 

capturing

 

the

 

relationships

 

between

 

interventions

 

and

anatomical

 

outcomes

 

(Fig.

 

5A–B’).

 

PlanForm

 

formalized

 

both

 

the

possible

 

functional

 

experiments

 

in

 

the

 

planarian

 

model

 

system

(including

 

gene

 

targeting,

 

surgical

 

cut/paste

 

manipulations,

 

etc.),

and

 

possible

 

planarian

 

body

 

configurations

 

as

 

outcomes

 

of

 

such

experiments

 

(via

 

graph

 

representations)

 

[169,170].

 

The

 

database

currently

 

contains

 

most

 

of

 

the

 

papers

 

in

 

the

 

planarian

 

research

field,

 

matching

 

published

 

experiments

 

to

 

their

 

patterning

 

out-

comes.

 

This

 

expert

 

system

 

not

 

only

 

allows

 

new

 

workers

 

in

 

the

field

 

to

 

rapidly

 

determine

 

what

 

has

 

been

 

done

 

and

 

what

 

the

 

out-

comes

 

result

 

from

 

specific

 

manipulations,

 

but

 

also

 

forms

 

the

 

body

of

 

knowledge

 

against

 

which

 

models

 

of

 

regeneration

 

can

 

be

 

for-

mally

 

tested,

 

to

 

determine

 

how

 

well

 

they

 

recapitulate

 

the

 

known

dynamics

 

of

 

planarian

 

repair.

 

This

 

database

 

is

 

a

 

flexible,

 

general

knowledge

 

system

 

to

 

which

 

newly

 

published

 

papers’

 

results

 

can

be

 

continuously

 

added

 

resulting

 

in

 

a

 

standardized

 

resource

 

akin

 

to

UniProt

 

for

 

molecular

 

data.

3.3.

 

Tools

 

for

 

model

 

discovery

Environments

 

are

 

coming

 

on-line

 

for

 

quantitative

 

simulations

of

 

available

 

models,

 

whose

 

predictions

 

can

 

be

 

tested

 

against

 

prior

data.

 

Anterior-posterior

 

patterning

 

and

 

re-scaling

 

have

 

been

 

solved

by

 

models

 

created

 

in

 

the

 

traditional

 

way

 

[125,140,145,146,171].

However,

 

future

 

work

 

will

 

also

 

have

 

to

 

deal

 

with

 

the

 

full

 

range

 

of

remarkable

 

capabilities

 

of

 

planarian

 

regeneration.

 

Arbitrary

 

cuts

and

 

punctures

 

require

 

a

 

worm

 

to

 

restore

 

precisely-patterned

 

and

intricate

 

shapes

 

such

 

as

 

intestinal

 

branches,

 

brains,

 

pharynxes,

and

 

numerous

 

other

 

tissues/organ

 

systems

 

in

 

the

 

right

 

number,

shape,

 

placement,

 

and

 

orientation

 

(stopping

 

precisely

 

when

 

the

right

 

shape

 

is

 

complete).

 

A

 

bio-realistic

 

model

 

combining

 

molecular

genetics

 

and

 

biophysics

 

to

 

quantitatively

 

explain

 

the

 

full

 

spectrum

of

 

planarian

 

regenerative

 

capabilities

 

is

 

likely

 

to

 

be

 

incredibly

 

com-

plex.

 

Moreover,

 

the

 

rapidly

 

increasing

 

dataset

 

on

 

perturbations

 

and

outcomes

 

in

 

this

 

(and

 

other

 

systems)

 

makes

 

it

 

increasingly

 

more

difficult

 

for

 

scientists

 

to

 

come

 

up

 

with

 

models

 

that

 

exhibit

 

the

 

cor-

rect

 

large-scale

 

patterning

 

behavior

 

from

 

a

 

specification

 

of

 

cellular

pathways

 

and

 

include

 

all

 

available

 

results.

 

Thus,

 

it

 

is

 

likely

 

that

 

this

field,

 

as

 

many

 

others,

 

is

 

poised

 

to

 

benefit

 

from

 

machine-learning

approaches

 

 

using

 

techniques

 

from

 

artificial

 

intelligence

 

to

 

assist

human

 

scientists

 

in

 

model

 

discovery

 

(a

 

branch

 

of

 

the

 

nascent

 

Robot

Scientist

 

field

 

[172]).

A

 

recent

 

example

 

of

 

model

 

inference

 

from

 

functional

 

data

 

[168]

used

 

evolutionary

 

search

 

algorithms

 

(progressive

 

rounds

 

of

 

eval-

uation

 

and

 

proportional

 

mutation

 

of

 

candidate

 

models,

 

Fig.

 

5C)

 

to

rapidly

 

uncover

 

a

 

small

 

biochemical

 

network

 

whose

 

behavior

 

in

a

 

biochemical

 

simulator

 

matched

 

key

 

regeneration

 

experiments

on

 

anterior-posterior

 

polarity

 

(Fig.

 

5D,

 

D’).

 

Surprisingly,

 

this

 

first

example

 

of

 

a

 

non-human-derived

 

model

 

in

 

regenerative

 

biology

resulted

 

in

 

a

 

fully-specified

 

model

 

that

 

is

 

simple

 

enough

 

for

 

human

scientists

 

to

 

understand

 

(unlike

 

neural

 

network

 

approaches,

 

the

result

 

is

 

not

 

simply

 

a

 

black

 

box

 

that

 

gets

 

the

 

answers

 

correct

 

but

provides

 

limited

 

insight

 

into

 

the

 

dynamics

 

involved)

 

(Fig.

 

5D).

 

The

use

 

of

 

evolutionary

 

principles

 

to

 

identify

 

models

 

with

 

desired

 

func-

tional

 

properties

 

(the

 

process

 

by

 

which

 

real

 

planaria

 

arose)

 

is

 

a

powerful

 

strategy

 

for

 

future

 

work,

 

as

 

the

 

search

 

can

 

be

 

re-run

 

as

new

 

data

 

appear

 

in

 

the

 

database,

 

and

 

more

 

powerful

 

simulators

come

 

on-line.

 

The

 

latter

 

is

 

important

 

as

 

a

 

major

 

limitation

 

of

 

this

work

 

is

 

that

 

the

 

machine-learning-derived

 

models

 

did

 

not

 

include

physiological

 

components

 

and

 

have

 

not

 

yet

 

been

 

tested

 

in

 

spatially-

realistic

 

simulators

 

to

 

probe

 

all

 

of

 

their

 

patterning

 

properties.

Such

 

machine-learning

 

approaches

 

can

 

uncover

 

networks

 

with

unknown

 

components.

 

Fortunately,

 

computational

 

techniques

 

are

now

 

available

 

to

 

provide

 

putative

 

identities

 

for

 

unknown

 

elements,

that

 

can

 

be

 

tested

 

in

 

model

 

validation

 

[173].

 

This

 

strategy

 

was

 

used

to

 

identify

 

one

 

of

 

the

 

novel

 

elements

 

in

 

the

 

planarian

 

network

as

 

the

 

HNF4

 

factor,

 

which

 

subsequent

 

functional

 

testing

 

via

 

RNAi

confirmed

 

[174].

An

 

important

 

aspect

 

of

 

this

 

effort

 

is

 

being

 

able

 

to

 

invert

 

the

 

mod-

els:

 

using

 

them

 

to

 

predict

 

functional

 

interventions

 

that

 

will

 

have

 

a

specific

 

desired

 

outcome.

 

This

 

is

 

an

 

essential

 

component

 

of

 

lever-

aging

 

model

 

systems

 

such

 

as

 

planaria

 

for

 

progress

 

in

 

regenerative

medicine.

 

An

 

example

 

of

 

this

 

is

 

the

 

recent

 

automated

 

discovery

 

of

 

a

bioelectric

 

and

 

serotonergic

 

signaling

 

network

 

explaining

 

stochas-

tic

 

conversion

 

of

 

normal

 

pigment

 

cells

 

to

 

a

 

melanoma

 

phenotype

by

 

bioelectrical

 

disregulation

 

[175].

 

While

 

the

 

molecular

 

signaling

components

 

at

 

the

 

cell-level

 

were

 

known

 

[123],

 

the

 

system-level

dynamics

 

were

 

unclear.

 

For

 

any

 

specific

 

disruption

 

of

 

bioelectric

signaling

 

among

 

somatic

 

cells,

 

some

 

percentage

 

of

 

the

 

animals

 

in

 

a

cohort

 

converted

 

normal

 

melanocytes

 

to

 

a

 

melanoma-like

 

behav-

ior.

 

What

 

was

 

completely

 

unclear

 

was

 

how

 

all

 

of

 

the

 

cells

 

within

a

 

certain

 

animal

 

coordinated

 

their

 

decision

 

to

 

react

 

in

 

an

 

all-or-

none

 

manner,

 

why

 

animals

 

treated

 

in

 

the

 

same

 

dish

 

exhibited

distinct

 

outcomes,

 

and

 

how

 

the

 

number

 

of

 

affected

 

animals

 

in

 

any

specific

 

perturbation

 

could

 

be

 

predicted.

 

This

 

problem

 

has

 

impor-

tant

 

parallels

 

understanding

 

the

 

stochastic

 

outcomes

 

of

 

planaria

in

 

which

 

bioelectrical

 

systems

 

have

 

been

 

altered

 

[132].

 

A

 

com-

putational

 

search

 

analyzed

 

the

 

network

 

as

 

a

 

dynamical

 

system,

identified

 

the

 

main

 

drivers

 

that

 

lock

 

the

 

system

 

into

 

one

 

of

 

several

global

 

attractor

 

states,

 

and

 

proposed

 

an

 

intervention

 

(consisting

 

of

two

 

drugs

 

and

 

one

 

specific

 

protein

 

misexpression)

 

that

 

would

 

break

the

 

concordance

 

[176].

 

Testing

 

confirmed

 

the

 

prediction,

 

producing

the

 

first

 

partially-hyperpigmented

 

animals

 

[177].

 

Future

 

develop-

ments

 

in

 

the

 

field

 

of

 

planarian

 

regeneration

 

will

 

likewise

 

make

 

use

of

 

dynamical

 

systems

 

analysis

 

and

 

computational

 

model

 

inference

to

 

not

 

only

 

identify

 

explanatory

 

models

 

but

 

also

 

identify

 

specific

interventions

 

to

 

drive

 

desired

 

morphological

 

outcomes

 

in

 

the

 

con-

text

 

of

 

increasingly-complex

 

regulatory

 

networks.

 

Applicability

 

of

other

 

frameworks,

 

such

 

as

 

P-systems

 

[178],

 

agent-based

 

dynamics

of

 

target

 

morphology

 

[179–181]

 

and

 

connectionist

 

models

 

[141]

remains

 

to

 

be

 

investigated.

(D)

 

This

 

process

 

uncovered

 

a

 

gene

 

regulatory

 

network

 

whose

 

patterning

 

properties

 

matched

 

observed

 

data

 

on

 

canonical

 

pathways

 

(a

 

sample

 

is

 

shown

 

in

 

D’).

Panels

 

A-D’

 

are

 

used

 

with

 

permission

 

from

 

[168,169].

914327a74b2eb3aa0c9c512d8ed73e06-html.html

138

 

M.

 

Levin

 

et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

87

 

(2019)

 

125–144

4.

 

Conclusion

Planaria

 

reflect

 

many

 

of

 

the

 

fundamental

 

mysteries

 

facing

 

us

 

in

the

 

new

 

century

 

of

 

interdisciplinary

 

biology.

 

As

 

a

 

model

 

amenable

to

 

molecular-genetic,

 

developmental,

 

regenerative,

 

and

 

behavioral

research,

 

this

 

remarkable

 

model

 

species

 

is

 

at

 

the

 

intersection

of

 

not

 

only

 

evolutionary

 

biology

 

and

 

biomedicine

 

but

 

also

 

syn-

thetic

 

bioengineering

 

and

 

information

 

science.

 

We

 

argue

 

that

 

one

of

 

the

 

main

 

benefits

 

of

 

this

 

model

 

is

 

to

 

facilitate

 

a

 

focus

 

not

only

 

on

 

the

 

mechanisms

 

that

 

control

 

regeneration,

 

but

 

also

 

on

the

 

algorithms

 

and

 

information-processing

 

mechanisms

 

imple-

mented

 

in

 

planarian

 

tissues.

 

Some

 

of

 

the

 

most

 

exciting

 

advances

in

 

the

 

biosciences

 

revolve

 

around

 

morphogenetic

 

engineering

[31,32],

 

morphological

 

computation

 

[34,182,183],

 

and

 

cellular

perception/decision-making

 

[184–186].

 

Planaria

 

represent

 

a

 

proof

of

 

principle

 

of

 

a

 

remarkable

 

“computational

 

medium”

 

 

a

 

mate-

rial

 

that

 

actively

 

fulfills

 

a

 

complex

 

design

 

spec

 

while

 

itself

 

being

drastically

 

remodeled,

 

and

 

a

 

system

 

in

 

which

 

the

 

control

 

circuitry

and

 

the

 

body

 

it

 

controls

 

are

 

one

 

and

 

the

 

same.

 

Planaria

 

are

 

an

 

ideal

lens

 

through

 

which

 

to

 

develop

 

new

 

techniques,

 

data,

 

and

 

concep-

tual

 

approaches

 

to

 

advance

 

the

 

intersection

 

of

 

these

 

fields,

 

with

numerous

 

applications

 

for

 

the

 

biomedicine

 

of

 

anatomical

 

control

and

 

the

 

understanding

 

of

 

the

 

relationship

 

between

 

genome

 

and

anatomy.

Given

 

that

 

the

 

genome

 

directly

 

encodes

 

proteins,

 

not

 

anatomi-

cal

 

structures,

 

how

 

do

 

tissues

 

store

 

information

 

about

 

the

 

pattern

to

 

which

 

they

 

must

 

regenerate?

 

By

 

regenerating

 

from

 

pieces

 

(no

obligate

 

Weismann’s

 

barrier),

 

planaria

 

are

 

helping

 

to

 

reveal

 

new

perspectives

 

on

 

the

 

question

 

of

 

where

 

anatomical

 

pattern

 

is

 

speci-

fied

 

[45,61,187].

 

Planaria

 

with

 

a

 

normal

 

histological

 

configuration

and

 

genome

 

can

 

permanently

 

store

 

(and

 

regenerate

 

to)

 

one

 

of

several

 

target

 

morphologies

 

[132].

 

The

 

recent

 

work

 

on

 

bioelectri-

cal

 

re-specification

 

of

 

pattern

 

memory,

 

producing

 

permanent

 

lines

of

 

double-headed

 

or

 

stochastically-destabilized

 

(cryptic)

 

planaria,

may

 

have

 

important

 

implications

 

for

 

the

 

evolution

 

of

 

bodyplans

[52,61].

 

Future

 

work

 

will

 

determine

 

to

 

what

 

extent

 

evolution

exploits

 

the

 

plasticity

 

of

 

physiological

 

software

 

in

 

concert

 

with

classical

 

genetic

 

change,

 

in

 

the

 

implementation

 

of

 

bodies

 

and

 

their

repair

 

circuits

 

[188].

 

Here,

 

we

 

have

 

argued

 

that

 

the

 

dynamics

 

of

regenerating

 

planaria

 

offer

 

an

 

ideal

 

system

 

in

 

which

 

to

 

quantita-

tively

 

integrate

 

the

 

perspectives

 

of

 

molecular-genetics,

 

dynamical

systems

 

theory,

 

biophysical

 

self-organizing

 

processes,

 

and

 

com-

putation.

 

Recent

 

approaches

 

provide

 

rich

 

fodder

 

for

 

this

 

effort,

including

 

advances

 

in

 

the

 

mechanisms

 

of

 

bioelectrical

 

pattern

 

con-

trol

 

and

 

biorealistic

 

modeling

 

that

 

facilitates

 

machine

 

learning

approaches

 

to

 

model

 

discovery

 

and

 

extraction

 

of

 

systems-level

insights

 

from

 

molecular

 

mechanisms.

Importantly,

 

future

 

efforts

 

must

 

begin

 

to

 

expand

 

from

 

AP

polarity

 

and

 

head

 

number,

 

to

 

understanding

 

of

 

actual

 

shape

 

(of

species-specific

 

heads,

 

and

 

overall

 

planarian

 

anatomy

 

in

 

three

dimensions).

 

At

 

the

 

moment,

 

our

 

understanding

 

of

 

planarian

shape

 

is

 

insufficient

 

to

 

derive

 

planaria-specific

 

morphologies

 

from

genomic

 

or

 

any

 

other

 

data.

 

A

 

focus

 

on

 

shape

 

is

 

essential,

 

not

only

 

because

 

of

 

the

 

demonstrated

 

multi-stability

 

of

 

the

 

planarian

regenerative

 

outcome

 

but

 

because

 

sometimes

 

molecular

 

marker

expression

 

and

 

anatomy

 

diverge;

 

for

 

example

 

tail

 

markers

 

can

 

be

expressed

 

in

 

tissues

 

that

 

have

 

the

 

overall

 

shape

 

of

 

heads

 

[151],

challenging

 

the

 

community

 

to

 

be

 

explicit

 

of

 

what

 

criteria

 

are

 

con-

sidered

 

the

 

gold

 

standard

 

by

 

which

 

“identity”

 

of

 

a

 

structure

 

can

 

be

determined.

Specific

 

directions

 

for

 

future

 

research

 

provide

 

a

 

fertile

 

ground

for

 

new

 

scientists

 

entering

 

this

 

field,

 

linking

 

planarian

 

regeneration

to

 

profound

 

directions

 

facing

 

biology

 

at

 

large.

 

The

 

understanding

of

 

variability

 

is

 

one;

 

how

 

can

 

clonal

 

animals,

 

raised

 

in

 

the

 

same

container

 

and

 

exposed

 

to

 

the

 

same

 

reagent/stimuli

 

in

 

the

 

same

dish,

 

exhibit

 

such

 

different

 

responses,

 

as

 

observed

 

in

 

the

 

stochas-

tic

 

outcomes

 

of

 

bioelectric

 

modulation

 

[132]

 

and

 

in

 

the

 

behavioral

responses

 

in

 

memory

 

and

 

drug

 

addiction

 

research

 

[24,25,189]?

Robustness

 

is

 

another:

 

despite

 

the

 

huge

 

variability

 

in

 

cell

 

num-

ber,

 

damage

 

type,

 

and

 

genetics,

 

planaria

 

reliably

 

exhibit

 

unfailing

anatomical

 

homeostasis.

 

This

 

kind

 

of

 

goal-directed

 

process,

 

able

 

to

harness

 

individual

 

cell

 

behaviors

 

toward

 

the

 

anatomical

 

needs

 

of

the

 

host

 

organism,

 

poses

 

a

 

fascinating

 

design

 

challenge

 

not

 

only

 

for

biologists

 

but

 

also

 

for

 

roboticists

 

and

 

engineers

 

seeking

 

to

 

improve

on

 

today’s

 

brittle

 

technology.

It

 

is

 

clear

 

that

 

biologists

 

will

 

have

 

to

 

expand

 

not

 

only

 

the

 

toolk-

its

 

(for

 

example,

 

bringing

 

(opto)genetics

 

to

 

planaria)

 

but

 

also

 

the

conceptual

 

apparatus,

 

if

 

we

 

hope

 

to

 

understand

 

what

 

regenerating

planaria

 

are

 

telling

 

us

 

about

 

biology.

 

It

 

remains

 

to

 

be

 

seen

 

which

type

 

of

 

paradigm

 

for

 

understanding

 

pattern

 

memory

 

and

 

its

 

elabo-

ration

 

during

 

regeneration

 

will

 

be

 

the

 

most

 

effective;

 

connectionist

and

 

Least

 

Action/Active

 

Inference

 

ideas

 

from

 

neuroscience

 

and

physics

 

[190–192],

 

are

 

possible

 

candidates.

 

Importantly

 

however,

the

 

explosion

 

of

 

molecular,

 

genetic,

 

physiological,

 

and

 

functional

data

 

in

 

this

 

field

 

also

 

provide

 

a

 

context

 

for

 

learning

 

to

 

extract

 

wis-

dom

 

and

 

actionable

 

intelligence

 

from

 

large

 

volumes

 

of

 

data.

 

New

efforts

 

in

 

the

 

bioinformatics

 

of

 

shape,

 

with

 

experimental

 

testing

in

 

the

 

planarian

 

model,

 

will

 

facilitate

 

the

 

contributions

 

of

 

artifi-

cial

 

intelligence

 

to

 

assist

 

human

 

researchers

 

in

 

cracking

 

the

 

secrets

of

 

planaria

 

and

 

exploiting

 

them

 

for

 

unprecedented

 

advances

 

in

biomedicine.

Acknowledgements

We

 

thank

 

the

 

Joshua

 

LaPalme,

 

other

 

members

 

of

 

the

 

Levin

lab,

 

Emili

 

Salò,

 

and

 

many

 

members

 

of

 

the

 

planarian

 

community

for

 

helpful

 

discussions,

 

and

 

Joshua

 

Finkelstein

 

for

 

comments

 

on

the

 

manuscript.

 

This

 

paper

 

is

 

dedicated

 

to

 

the

 

memory

 

of

 

C.

 

M.

Child,

 

G.

 

Marsh,

 

and

 

H.

 

W.

 

Beams

 

 

original

 

pioneers

 

in

 

the

 

phys-

iology

 

of

 

planarian

 

regeneration.

 

This

 

work

 

was

 

supported

 

by

an

 

Allen

 

Discovery

 

Center

 

award

 

from

 

The

 

Paul

 

G.

 

Allen

 

Fron-

tiers

 

Group

 

(12171).

 

The

 

authors

 

gratefully

 

acknowledge

 

support

from

 

the

 

National

 

Institutes

 

of

 

Health

 

(AR055993,

 

AR061988),

 

the

G.

 

Harold

 

and

 

Leila

 

Y.

 

Mathers

 

Charitable

 

Foundation

 

(TFU141),

National

 

Science

 

Foundation

 

award#CBET-0939511,

 

the

 

W.

 

M.

KECK

 

Foundation

 

(5903),

 

and

 

the

 

Templeton

 

World

 

Charity

 

Foun-

dation

 

(TWCF0089/AB55).

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et

 

al.

 

/

 

Seminars

 

in

 

Cell

 

&

 

Developmental

 

Biology

 

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(2019)

 

125–144

 

139

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Bardeen,

 

F.H.

 

Baetjer,

 

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of

 

the

 

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regeneration

 

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L.P.

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F.V.

 

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Cleft

 

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Moderate

 

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914327a74b2eb3aa0c9c512d8ed73e06-html.html

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potassium

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M.

 

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C.

 

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N.

 

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S.P.

 

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The

 

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potassium

 

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Flex,

 

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G.J.

 

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Y.

 

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J.

 

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G.

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S.

 

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J.G.

 

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S.M.

 

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J.

 

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G.F.

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Mutations

 

in

 

the

 

voltage-gated

 

potassium

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KCNH1

 

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J.R.

 

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I.K.

 

Kong,

 

L.C.

 

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H.G.

 

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A

 

microdeletion

 

at

 

Xq22.2

 

implicates

 

a

 

glycine

 

receptor

 

GLRA4

 

involved

 

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G.

 

Klement,

 

P.

Arhem,

 

M.

 

Schalling,

 

C.

 

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-

 

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J.R.

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K.J.

 

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N.T.

 

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J.C.

 

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H.K.

 

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J.D.

 

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J.

 

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D.A.

 

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De

 

novo

 

mutations

 

in

 

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limbs

 

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Cystic

 

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gene

 

mutations

 

in

 

infertile

 

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E.

 

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Martin,

 

M.

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Mutations

 

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Recovery

 

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J.

 

Zlotogora,

 

Z.

 

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G.

 

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S.

 

Khateeb,

 

N.

 

Zilberberg,

 

O.S.

 

Birk,

 

Maternally

 

inherited

 

Birk

 

Barel

mental

 

retardation

 

dysmorphism

 

syndrome

 

caused

 

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genomically

 

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Bruining,

 

A.S.

 

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J.M.

 

Silva,

 

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T.M.

 

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Mackay,

 

J.P.

 

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van

 

Rhijn,

 

J.K.

 

Wales,

 

P.

 

Clark,

 

S.

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J.

 

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P.R.

 

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Activating

 

mutations

 

in

 

the

 

gene

 

encoding

 

the

 

ATP-sensitive

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C.

 

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knockout

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a

 

model

 

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Jervell

 

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Lange-Nielsen

 

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Jr,

 

S.G.

 

Sirenko,

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A.

 

Grinberg,

 

S.P.

 

Huang,

 

S.N.

 

Ebert,

 

K.

 

Pfeifer,

 

Targeted

 

point

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Kcnq1:

 

phenotypic

 

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K++

 

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Fu,

 

L.J.

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potassium

 

channel

 

Kir2.1

 

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Quitania,

 

J.H.

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B.L.

 

Miller,

 

Y.H.

 

Fu,

 

L.J.

 

Ptacek,

 

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Deficiency

 

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Mice

 

devoid

 

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type

 

a

 

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beta3

 

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have

 

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